System and method for three-way call detection

Information

  • Patent Grant
  • 10601984
  • Patent Number
    10,601,984
  • Date Filed
    Tuesday, March 27, 2018
    6 years ago
  • Date Issued
    Tuesday, March 24, 2020
    4 years ago
Abstract
A system for detecting three-way calls in a monitored telephone conversation includes a speech recognition processor that transcribes the monitored telephone conversation and associates characteristics of the monitored telephone conversation with a transcript thereof, a database to store the transcript and the characteristics associated therewith, and a three-way Call detection processor to analyze the characteristics of the conversation and to detect therefrom the addition of one or more parties to the conversation. The system preferably includes at least one domain-specific language model that the speech recognition processor utilizes to transcribe the conversation. The system may operate in real-time or on previously recorded conversations. A query and retrieval system may be used to retrieve and review call records from the database.
Description
BACKGROUND OF THE INVENTION

Field of the Invention


This invention relates generally to telephony, and more particularly to a method and system for detecting each time a party is added to a previously recorded or live telephone call.


Background Art


There is a general need for systems that detect three way calls. Many types of systems exist that detect three way calls by measuring certain line characteristics such as voltage fluctuations, noise and other electromechanical characteristics.


The purpose of many conventional three way call detection systems is to automatically disconnect an existing telephone connection whenever a three way call is detected. For example, correctional facilities such as jails and prisons routinely monitor or record telephone conversations of inmates. Inmates in general are prohibited from making three way telephone calls because these calls have been found to be made in order to, for instance, inappropriately contact witnesses or to call individuals that they would otherwise be prohibited from calling, such as convicted felons, drug dealers and gang members.


Although most correctional facilities record all telephone calls made by inmates, it is believed that on average only a very small proportion is ever monitored by correctional officers. Many correctional facilities record 2,000 inmate telephone calls a day, so monitoring all telephone calls or even a large fraction on a regular basis would require too much personnel and would be cost prohibitive. Inmates are aware of this and know that there is little chance of getting caught making a three way call. Thus, many make three way calls on a routine basis without being detected.


It is desirable in some instances to allow the completion of three way calls for intelligence gathering purposes because such calls often contain evidence of wrongdoing, such as terrorist or criminal activity, or other valuable information. Simply disconnecting an inmate as soon as a three way call is detected could result in the loss of potentially valuable information in those cases where an inmate is suspected of using telephones to engage in illegal or improper activity.


BRIEF SUMMARY OF THE INVENTION

It is therefore desirable to be able to search for and retrieve telephone calls that involved three way conversations in order to determine whether illegal activities were discussed.


It is also desirable to be able to go directly to the point in the conversation where the three way call took place without having to listen to the entire conversation.


The present invention provides a method and system for the detection, retrieval and playback of three way telephone calls based upon an analysis of the characteristics and patterns of the content of the telephone conversation. The invention is designed to provide an efficient means for organizations such as correctional facilities to identify and monitor the contents of three way conversations. The present invention leverages the discovery that three way conversations share a number of characteristics that can readily be detected, measured, analyzed, and input into computer algorithms which can then reliably determine whether a three way call was placed during a particular telephone conversation.


Disclosed herein is a system for detecting three-way calls in a monitored telephone conversation. The system includes a speech recognition processor that transcribes the monitored telephone conversation and associates at least one characteristic of the monitored telephone conversation with a transcript of the monitored telephone conversation. A database stores at least the transcript of the monitored telephone conversation and the at least one characteristic associated therewith. In some embodiments of the invention, the database also stores a recording of the monitored telephone conversation. A three-way call detection processor analyzes the at least one characteristic associated with the monitored telephone conversation to detect the addition of one or more third parties to the monitored telephone conversation.


The system preferably includes at least one domain-specific language model, such as a language model specific to inmate telephony, that the speech recognition processor utilizes to transcribe the monitored telephone conversation. The system may include multiple domain-specific language models trained for a plurality of ethnic groups, dialects, foreign languages, or other variations in speech and language patterns. Where foreign languages are involved, an optional translation processor may be utilized to translate the monitored telephone conversation or the transcript thereof.


In some embodiments of the invention, transcription and extraction of characteristics occurs in real-time (that is, while the monitored telephone conversation is in progress). Alternatively, the telephone conversations may be recorded and processed at a later time.


At least some of the characteristics extracted from the monitored telephone conversation are indicative of the addition of a third-party to the conversation, and thus of the establishment of a three-way call. For example, gaps in conversation, dial tones, dial pulses, ring tones, telephone salutations (“hello”), and other verbal and non-verbal cues or patterns may indicate that a third party has joined the conversation. The three-way call detection processor may detect these characteristics using any of a number of call-processing algorithms, including, without limitation; algorithms that measure the frequency of phrases uttered during the conversation; algorithms that measure the timing of phrases uttered during the conversation; algorithms that extract entities from phrases uttered during the conversation; pattern detection algorithms that compare timings of utterances within the conversation with timings of the at least one characteristic associated therewith; and any combinations thereof.


Based upon the characteristics and patterns of the monitored telephone conversation, a score may be assigned that is indicative of a likelihood that at least one third party was added to the monitored telephone conversation.


The present invention also provides a method of detecting three-way calls in a monitored telephone conversation. The method includes: transcribing the monitored telephone conversation; extracting a plurality of characteristics of the monitored telephone conversation; associating the extracted plurality of characteristics with a transcript of the monitored telephone conversation; utilizing a scoring algorithm to assign a score to the monitored telephone conversation based on the extracted plurality of characteristics, wherein the score is indicative of a likelihood that at least one third party was added to the monitored telephone conversation; and generating information regarding addition of a third party to the monitored telephone conversation. The scoring algorithm typically utilizes a scoring function, such as a logistic function or a threshold function, to calculate the score, but may also utilize an artificial neural network.


The information generated by the method may include tagging the transcript to identify portions thereof that are pertinent to the addition of the third party. In other embodiments of the invention, the information generated includes timestamps or word or character locations at which the three-way call likely begins. Of course, the transcript may also be associated with a sound recording of the monitored telephone conversation.


In another aspect of the invention, a method of detecting three-way calls in a monitored telephone conversation, includes the following steps: transcribing the monitored telephone conversation; extracting a plurality of characteristics of the monitored telephone conversation, wherein the plurality of characteristics extracted are indicators of a third party being added to the monitored telephone conversation; calculating a score for the monitored telephone conversation based upon at least two of the plurality of characteristics extracted from the monitored telephone conversation, wherein the score is indicative of a likelihood that at least one third party was added to the monitored telephone conversation; and generating information regarding addition of a third party to the monitored telephone conversation. Preferably, at least three of the plurality of characteristics, and more preferably at least four of the plurality of characteristics, will be used to calculate the score for the monitored telephone conversation.


Also disclosed herein is a query and retrieval system for monitored telephone conversations. The query and retrieval system includes a database of monitored telephone conversations including a plurality of call records. At least some of the call records include a recording of the monitored telephone conversation, a transcript of the monitored telephone conversation, and a score assigned to the monitored telephone conversation. The score reflects a likelihood or probability that the monitored telephone conversation included one or more three-way calls, and is based upon one or more characteristics or patterns of the monitored telephone conversation indicative of adding a third party thereto. The system also includes a query interface that accepts user input of search criteria, including at least a score criterion; a processor that retrieves one or more call records from the database based upon the search criteria; a selection interface that permits user selection of a call record from the retrieved one or more call records; and an output device that provides synchronized playback and visualization, respectively, of the recording and transcript included in the selected call record. The output device optionally provides a visual identification of the one or more characteristics of the monitored telephone conversation indicative of adding a third party thereto. In some embodiments of the invention, the system further includes an interface that permits direct navigation to one or more three-way calls within the monitored telephone conversation.


An advantage of the present invention is that it operates independently of any particular type of telephone system, such as VOIP or POTS.


Another advantage of the present invention is that it is not necessary to Modify existing telephone equipment or add hardware devices.


Still another advantage of the present invention is that it can detect three way calls more accurately than previous methods.


Yet another advantage of the present invention is that it can provide detailed information as to the nature and content of the three way call.


Another advantage of the present invention is that it permits one to “jump” directly to the point in the conversation where the three-way call likely occurred.


Still another advantage is that the present system permits the review of a large number of recorded phone calls using only a small number of personnel.


A further advantage of the present invention is that it permits the collection of intelligence information to help uncover inappropriate activities, such as acts of terrorism.


The foregoing and other aspects, features, details, utilities, and advantages of the present invention will be apparent from reading the following description and claims, and from reviewing the accompanying drawings.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is a block diagram illustrating a speech recognition system according to an embodiment of the present invention.



FIG. 2 is a flow diagram illustrating a three way call detection method according to an embodiment of the present invention.



FIG. 3 is a drawing illustrating a scoring algorithm according to an embodiment of the present invention.



FIG. 4 is a block diagram illustrating a query and retrieval system according to an embodiment of the present invention.



FIG. 5 is a sample output screen according to an embodiment of the query and retrieval system of the present invention.





DETAILED DESCRIPTION OF THE INVENTION

The following description illustrates the invention with reference to a real world domain, and in particular with respect to correctional facilities. However, it should be understood that the practice of the invention is not limited to any particular domain or application. Rather, it is intended to be more generally applicable to any domain where there is a need for three way call detection by examining the contents and call characteristics of recorded or live telephone conversations.


Unlike traditional three-way call detection systems, the primary purpose of which is to terminate a telephone conversation upon establishment of a three-way call, the present invention identifies the three-way call and allows the call to proceed. A user of the system, such as a corrections officer or investigator, may then search for monitored calls during which at least one three-way call took place, retrieve such a call, and proceed directly to the point where a third party was added, for example to gather intelligence or evidence of inappropriate conduct.


The following is a portrayal of one example of a typical sequence of events that might occur when an inmate or similar person makes a three way call:


1. The inmate first places a call to a telephone number that is not blocked or otherwise restricted by the correctional facility. The recipient of the call answers the phone.


2. At some point during the telephone conversation, the inmate asks the recipient to place a three way call. When making this request, the inmate might dictate a telephone number or mention the name or organization of the third party to call.


3. The recipient then might put the inmate on hold while the call is being made. Alternatively, the recipient might use a separate phone (e.g., a separate cell phone) to place the three way call. Certain call characteristics suggestive of a three-way call, such as a gap in speech or a series of dial tones or pulses, may be observed, and may possibly be followed by a ring tone.


4. The third party answers the phone and commences conversing with the inmate. The third party will typically answer with a phrase commonly used when answering the telephone such as “hello,” “hi,” or another salutation or greeting, or stating the name of a person or organization. At this point a speaker recognition (or speaker turn) component of the speech recognition system may detect that a third and previously undetected speaker has been added to the call. It is important to note that the addition of a third and previously undetected speaker in the conversation by itself is not dispositive of a three way call. It could simply be another member of the household of the original recipient of the call.


5. After a while the inmate or three way call recipient decides to terminate the three way call. At this point the inmate may do one of three things: terminate the original call, continue to converse with the original recipient, or make another three way call.


6. If another three way call is made, then the same or similar sequence of events from (1) through (5) applies.


The present invention focuses on and analyzes the three way call characteristics described above (e.g., verbal and non-verbal cues, such as telephone numbers, names of people or organizations to call, salutations, dial tones, dial pulses, ring tones, gaps in conversation, speaker turns/recognition of new speakers, and other three way call characteristics). A speech recognition processor, in combination with a plurality of other algorithms and processors, searches the monitored call for clues that would indicate the addition of one or more third parties to a telephone conversation, while a three-way call detection processor evaluates the clues to determine a probability that the call included at least one three-way call.



FIG. 1 illustrates a block diagram of a speech recognition system 100 according to an embodiment of the present invention. The speech recognition system 100 may be software-implemented (e.g., a software program executed by one or more computer systems or speech recognition processors), hardware-implemented (e.g., as a series of instructions stored in one or more solid-state devices), or a combination of both. It should also be understood that multiple instances of the speech recognition system 100 may be simultaneously executed on a single computer or on multiple computers.


In FIG. 1, the speech recognition process 104 processes previously recorded telephone conversations 106 or ongoing telephone conversations 102, which are referred to interchangeably herein as “monitored telephone conversations,” “telephone conversations,” “conversations,” or simply “calls.” The speech recognition process 104 performs a number of functions, of which one is converting the spoken audio to text (transcription). In doing so, the speech recognition process utilizes at least one language model 108. When transcribing speech to text, it is desirable to ensure that the language model used is domain-specific, which enhances the accuracy of the transcription process. A “domain-specific” language model is a language model that accurately reflects the linguistic nuances of the participants of a telephone conversation, for example a language model that is domain-specific to inmate telephony. Preferably, therefore, at least one domain specific language model is used by the system in transcribing the audio of the monitored telephone conversation to text.


In some embodiments of the invention, it is contemplated that multiple domain-specific language models may be used, which may be trained for a plurality of ethnic groups, a plurality of regional dialects, or other language differences. Using multiple domain-specific language models has been shown to significantly improve speech recognition and transcription accuracy. It is also contemplated that, in instances where foreign languages are spoken, multiple domain-specific language models trained for a plurality of foreign languages may be used. Further, a translation processor may be utilized to translate the transcript of the monitored telephone conversation from a first language to a second language (e.g., to translate a conversation in Spanish into English).


In addition to converting spoken audio to text, the speech recognition process extracts a number of verbal and non-verbal characteristics from the telephone conversation. These include, but are not limited to, speaker turns (e.g., as determined by voice-recognition of the speakers in the telephone conversation); gaps in audio; dial, pulse, and ring tones; verbal cues (e.g., mentions of telephone numbers, mentions of people or organizations to call, or telephone salutations such as “hello”); speech and phraseology patterns; and timing information that includes the beginning and end times (e.g., as measured in either seconds or milliseconds from the beginning of the telephone conversation) of utterances, audio gaps, dial, pulse and ring tones, and speaker turns. The characteristics are preferably associated with the transcript of the monitored telephone conversation.


Once the speech recognition process has completed processing the telephone conversation, it outputs the results to a file. One suitable format for the output file is an XML file 112. The output file is then processed by an output processor 110 that extracts each component from the XML file and inserts it as a call record into a multimedia database 114, for example as a binary large object (BLOB). That is, as illustrated in FIG. 1, the multimedia database stores the transcript of the monitored telephone conversation, the associated characteristics of the monitored telephone conversation, and, in some embodiments of the invention, a sound recording of the monitored telephone conversation. The sound recording may, of course, be analog or digital. It is also within the spirit and scope of the present invention to store the recordings of the monitored telephone conversation in a file system external to the multimedia database, in which case the multimedia database preferably includes appropriate references to the external file system. The telephone conversation is now ready for three way call detection.



FIG. 2 illustrates a flow chart diagram of a three way call detection method 200 according to an embodiment of the present invention. As with the speech recognition method illustrated in FIG. 1 and described above, it is contemplated that the three-way call detection method illustrated in FIG. 2 and described below may be hardware-implemented, software-implemented, or both hardware- and software-implemented. It is also contemplated that multiple instances of the three-way call detection method may run simultaneously on one or more computers or by one or more three-way call detection processors.


In the embodiment illustrated in FIG. 2, the three way call detection method begins by utilizing a call retrieval process 202 to retrieve a previously unprocessed call from the multimedia database 114. The transcribed text, as well as information regarding the associated call characteristics, may be forwarded to the call processing algorithms 210. The call processing algorithms employ a variety of techniques including, but not limited to: entity extraction (e.g., algorithms that extract people, places, organizations, telephone numbers and other entities from the transcribed text); algorithms that measure the frequency of phrases uttered during the call; algorithms that measure the timing of phrases uttered during the call; pattern detection techniques that compare the timing of phrases and entities uttered with the timing of extracted telephone conversation characteristics such as gaps in speech, dial tones, pulses and speaker turns; and any combinations thereof For example, one call processing algorithm may compare the timing of the utterance of a telephone number with the timing of a gap in the conversation; if the two are close in time (e.g., a telephone number is uttered and, a few seconds later, a gap in the conversation occurs), it may indicate establishment of a three-way call.


The call processing algorithms seek characteristics or patterns indicative of a three-way call. If any characteristics or patterns indicative of a three-way call are identified at a particular point in the telephone conversation, the call processing algorithms may output a matrix of the identified characteristics and/or patterns, along with timing information and a weighting structure that weights each pattern and characteristic with respect to its relative importance as a characteristic or pattern indicative of a three-way call. For example, a gap in the conversation may have a lower weight than an utterance of a telephone number followed by a gap in the conversation, which may have a lower weight than an utterance of a telephone number followed by a gap in the conversation in which dial tones are detected, which may have a lower weight than an utterance of a telephone number followed by a gap in the conversation in which dial tones are detected and after which a new speaker is identified by the speech recognition processor. The output matrix is then forwarded to the scoring algorithm 208. If no three way call characteristics are identified, control is returned to the call retrieval process, which then retrieves the next unprocessed telephone conversation from the multimedia database.


The scoring algorithm 208 then computes a score based on the output matrix of the extracted call characteristics and patterns. The score is indicative of a likelihood or probability that at least one third party was added to the monitored telephone conversation. In some embodiments of the invention, the score is computed as follows:







S
=




i
=
1

n




w
i



x
i




,





where S is the interim score, n is the number of characteristics and patterns in the output matrix of the call processing algorithms 210, wi is the weight of the ith characteristic or pattern in the matrix, and xi is the ith characteristic or pattern in the matrix. Note that xi can be represented by binary, integer, or continuous values. In other embodiments of the invention, the scoring algorithm may calculate the value of S using an associative artificial neural network, such as an associative network, for example Kohonen networks.


The interim score S may optionally be further refined by the use of a logistic function to produce values of between zero and one as follows:








S





F

=

1

1
+

e

-
s





,





where SF is the final score. A graphical representation of the two scoring equations outlined above is illustrated in FIG. 3, where F(S) is represented by the preceding equation. Also in FIG. 3, a bias is added to the calculation of S. If the interim score S is not further refined, then the interim score S becomes the final score SF.


Referring once again to FIG. 2, after the final score has been calculated, it is determined whether it exceeds a minimum requirement to indicate that a three way call took place. For example, the final score may be compared to a threshold in order to minimize the potential for false positives (e.g., identifications of three-way calls where no three-way calls in fact occurred). If the threshold is exceeded, control is passed to the tagging algorithm 206, which will be further described below. Otherwise, if the end of the current call has been reached, control is passed back to the call retrieval process 202. If the end of the call has not been reached, control is passed back to the call processing algorithms 210, which will then continue processing the call.


Upon successful detection of a three way call, information regarding the addition of a third party to the telephone conversation may be generated. For example, the tagging process 206 may tag the telephone conversation being processed as including at least one three-way call, and may also tag each phrase, pattern, or point in the transcript that is pertinent to a three way call. The tags may then be added to the multimedia database 114 as part of the call record along with the final score for the telephone conversation output by the scoring algorithm.


The tagging process 206 then passes control to the synchronization process 204. The synchronization process identifies the likely beginning of the detected three way call and updates the call record produced by the tagging process 206 with information regarding the likely beginning of the detected three-way call. For example, the synchronization process 204 may add a time stamp or other timing information that identifies the number of seconds from the beginning of the call at which the three-way call took place. The synchronization process 204 may also add information about how many words or characters into the transcript the three way call occurred. The multimedia database 114 may also be updated to include at least one index of detected three-way calls and the characteristics associated with those calls.


If the end of the call has been reached, control is returned to the call retrieval process. Otherwise, control is returned to the call processing algorithms.



FIG. 4 is a block diagram of a generalized query and retrieval system 400 according to an embodiment of the present invention that may be used to retrieve records from the multimedia database 114, and in particular may be used to retrieve records of three-way calls from the multimedia database 114. The call browser and display 402 of FIG. 4 may be used to query and retrieve detected three way calls from the multimedia database 114. The call browser and display 402 preferably accepts queries based upon scores. For example, a user may request calls having scores that fall within a particular range or that exceed a particular threshold. It is contemplated, however, that the call browser and display 402 may accept any type of criteria on which to search the multimedia database 114 (e.g., call dates, call times, or the identity of a party to the call). A suitably-programmed processor may retrieve one or more call records meeting the specified criteria from the multimedia database 114.



FIG. 5 shows a sample output screen 500 of a query and retrieval system according to the present invention. Panel 502 displays the call records retrieved in response to a particular query and permits a user to select one of the retrieved call records. Panel 504 shows the transcript of the selected call record. The “Play Call” button 506 will initiate playback of the recording of the selected telephone conversation. Preferably, the query and retrieval system includes an output device capable of providing synchronized playback and visualization, respectively, of the recording and transcript of the selected telephone conversation. For example, as the audio recording of the call is played back, a moving highlight may track through the transcript in panel 504.


The output device further preferably provides a visual identification of the one or more characteristics of the monitored telephone conversation that are indicative of the establishment of a three-way call. For example, the transcript in panel 504 may be highlighted or otherwise flagged at the point where the three-way call was established or throughout the three-way call (e.g., the entire portion of the transcript covering the three-way call may be bold faced). Similarly, the panel 508 may show a time stamp 510 of when in the call the three-way call was established, and, optionally, when it was terminated. In some embodiments of the invention, the time stamp 510 may be used to navigate directly (or “jump”) to the point in the conversation where the three-way call took place, thereby advantageously permitting law enforcement officials to review only the portion of the conversation that is of particular interest.


Although only a few illustrative embodiments of this invention have been described above with a certain degree of particularity, those skilled in the art could make numerous alterations to the disclosed embodiments without departing from the spirit or scope of this invention. It is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative only and not limiting. Changes in detail or structure may be made without departing from the spirit of the invention as defined in the appended claims.

Claims
  • 1. A system for detecting a three-way call in a monitored telephone conversation, the system comprising: one or more circuits configured to: generate a domain specific language model associated with a participant of the monitored telephone conversation;convert an audio of the monitored telephone conversation to a transcript using the domain specific language model;detect an utterance of a phrase occurring at a first time within the monitored telephone conversation based on the transcript of the monitored telephone conversation;determine an audio characteristic occurring at a second time within the monitored telephone conversation; detect a pattern based on the phrase, the audio characteristic, and a comparison of the first time and the second time; anddetect the three-way call based on the pattern, wherein the pattern is an indicator of the three-way call.
  • 2. The system of claim 1, wherein the one or more circuits are further configured to: tag the transcript with a timestamp associated with detection of the three-way call in the monitored telephone conversation.
  • 3. The system of claim 1, wherein the one or more circuits are further configured to: store at least one of the audio and the transcript to a database.
  • 4. The system of claim 1, wherein to convert the audio of the monitored telephone conversation to the transcript using the domain specific language model, the one or more circuits are configured to: translate the transcript of the monitored telephone conversation from a first language into a second language.
  • 5. The system of claim 1, wherein to detect the three-way call based on the pattern, the one or more circuits are configured to: determine a value corresponding to the pattern; anddetermine that the value is greater than a predetermined threshold.
  • 6. The system of claim 1, wherein to generate the domain specific language model associated with the participant of the monitored telephone conversation, the one or more circuits are configured to train at least one of: the domain specific language model to transcribe inmate telephony;the domain specific language model to transcribe a foreign language; orthe domain specific language model to transcribe a linguistic dialect.
  • 7. The system of claim 1, wherein the pattern includes one or more of a frequency of the utterance of the phrase during the monitored telephone conversation, a timing of the utterance of the phrase during the monitored telephone conversation, or an entity extracted from the utterance of the phrase.
  • 8. The system of claim 1, wherein the monitored telephone conversation is one of a monitored voice over IP (VOIP) telephone call or a monitored plain old telephone system (POTS).
  • 9. A method for detecting a three-way call in a monitored telephone conversation, the method comprising: generating a domain specific language model associated with a participant of the monitored telephone conversation;converting an audio of the monitored telephone conversation to a transcript using the domain specific language model;detecting an utterance of a phrase occurring at a first time within the monitored telephone conversation based on the transcript of the monitored telephone conversation;determining an audio characteristic occurring at a second time within the monitored telephone conversation;detecting a pattern based on the phrase, the audio characteristic, and a comparison of the first time and the second time; anddetecting the three-way call based on the pattern, wherein the pattern is an indicator of the three-way call.
  • 10. The method according to claim 9, further comprising: storing at least one of the audio and the transcript to a database.
  • 11. The method according to claim 9, further comprising: tagging the transcript with a timestamp associated with detection of the three-way call in the monitored telephone conversation.
  • 12. The method according to claim 11, further comprising: providing a synchronized presentation of the audio and the transcript.
  • 13. The method according to claim 9, wherein converting the spoken audio of the monitored telephone conversation to the transcript using the domain specific language model comprises: translating the transcript of the monitored telephone conversation from a first language into a second language.
  • 14. The method according to claim 9, wherein detecting the three-way call based on the pattern comprises: determining a value corresponding to the pattern; anddetermining that the value is greater than a predetermined threshold.
  • 15. The method according to claim 9, wherein generating the domain specific language model associated with the participant of the monitored telephone conversation comprises at least one of: training the domain specific language model to transcribe inmate telephony;training the domain specific language model to transcribe a foreign language; ortraining the domain specific language model to transcribe a linguistic dialect.
  • 16. The method according to claim 9, wherein the pattern includes one or more of a frequency of the utterance of the phrase during the monitored telephone conversation, a timing of the utterance of the phrase during the monitored telephone conversation, or an entity extracted from the utterance of the phrase.
  • 17. A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations comprising: generating a domain specific language model associated with a participant of a monitored telephone conversation;converting an audio of the monitored telephone conversation to a transcript using the domain specific language model;detecting an utterance of a phrase occurring at a first time within the monitored telephone conversation based on the transcript of the monitored telephone conversation;determining an audio characteristic occurring at a second time within the monitored telephone conversation;detecting a pattern based on the phrase, the audio characteristic, and a comparison of the first time and the second time; anddetecting the three-way call based on the pattern, wherein the pattern is an indicator of the three-way call.
  • 18. The non-transitory computer-readable device of claim 17, the operations further comprising: tagging the transcript with a timestamp associated with detection of the three-way call in the monitored telephone conversation.
  • 19. The non-transitory computer-readable device of claim 18, the operations further comprising: providing a synchronized presentation of the audio and the transcript.
  • 20. The non-transitory computer-readable device of claim 17, wherein the pattern includes one or more of a frequency of the utterance of the phrase during the monitored telephone conversation, a timing of the utterance of the phrase during the monitored telephone conversation, or an entity extracted from the utterance of the phrase.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. application Ser. No. 15/265,510 filed Sep. 14, 2016, which is a continuation application of U.S. application Ser. No. 14/604,388 filed Jan. 23, 2015, which is a continuation of U.S. application Ser. No. 13/971,292, filed Aug. 20, 2013, now U.S. Pat. No. 8,942,356, which is a continuation application of U.S. application Ser. No. 11/706,431, filed Feb. 15, 2007, now U.S. Pat. No. 8,542,802, each of which is incorporated herein by reference in their entirety.

US Referenced Citations (298)
Number Name Date Kind
3406344 Hopper Oct 1968 A
3801747 Queffeulou et al. Apr 1974 A
3985956 Monti et al. Oct 1976 A
4028496 LaMarche et al. Jun 1977 A
4054756 Comella et al. Oct 1977 A
4191860 Weber Mar 1980 A
4670628 Boratgis et al. Jun 1987 A
4691347 Stanley et al. Sep 1987 A
4703476 Howard Oct 1987 A
4737982 Boratgis et al. Apr 1988 A
4813070 Humphreys et al. Mar 1989 A
4907221 Pariani et al. Mar 1990 A
4918719 Daudelin Apr 1990 A
4935956 Hellwarth et al. Jun 1990 A
4943973 Werner Jul 1990 A
4995030 Helf Feb 1991 A
5229764 Matchett et al. Jul 1993 A
5291548 Tsumura et al. Mar 1994 A
5319702 Kitchin et al. Jun 1994 A
5319735 Preuss et al. Jun 1994 A
5345595 Johnson et al. Sep 1994 A
5379345 Greenberg Jan 1995 A
5425091 Josephs Jun 1995 A
5438616 Peoples Aug 1995 A
5483593 Gupta et al. Jan 1996 A
5502762 Andrew et al. Mar 1996 A
5535194 Ashley et al. Jul 1996 A
5535261 Brown et al. Jul 1996 A
5539731 Haneda et al. Jul 1996 A
5539812 Kitchin et al. Jul 1996 A
5555551 Rudokas et al. Sep 1996 A
5583925 Bernstein Dec 1996 A
5590171 Howe Dec 1996 A
5592548 Sih Jan 1997 A
5613004 Cooperman Mar 1997 A
5619561 Reese Apr 1997 A
5623539 Bassenyemukasa et al. Apr 1997 A
5634086 Rtischev et al. May 1997 A
5636292 Rhoads Jun 1997 A
5640490 Hansen et al. Jun 1997 A
5646940 Hotto Jul 1997 A
5649060 Ellozy et al. Jul 1997 A
5655013 Gainsboro Aug 1997 A
5675704 Juang et al. Oct 1997 A
5687236 Moskowitz Nov 1997 A
5710834 Rhoads Jan 1998 A
5719937 Warren et al. Feb 1998 A
5745558 Richardson, Jr. et al. Apr 1998 A
5745569 Moskowitz Apr 1998 A
5745604 Rhoads Apr 1998 A
5748726 Unno May 1998 A
5748763 Rhoads May 1998 A
5748783 Rhoads May 1998 A
5757889 Ohtake May 1998 A
5768355 Salibrici et al. Jun 1998 A
5768426 Rhoads Jun 1998 A
5774452 Wolosewicz Jun 1998 A
5796811 McFarlen Aug 1998 A
5802145 Farris et al. Sep 1998 A
5805685 McFarlen Sep 1998 A
5809462 Nussbaum Sep 1998 A
5822432 Moskowitz Oct 1998 A
5822436 Rhoads Oct 1998 A
5822726 Taylor et al. Oct 1998 A
5832119 Rhoads Nov 1998 A
5835486 Davis et al. Nov 1998 A
5841886 Rhoads Nov 1998 A
5841978 Rhoads Nov 1998 A
5850481 Rhoads Dec 1998 A
5862260 Rhoads Jan 1999 A
5867562 Scherer Feb 1999 A
5883945 Richardson, Jr. et al. Mar 1999 A
5889568 Seraphim et al. Mar 1999 A
5889868 Moskowitz et al. Mar 1999 A
5899972 Miyazawa et al. May 1999 A
5907602 Peel et al. May 1999 A
5920834 Sih et al. Jul 1999 A
5926533 Gainsboro Jul 1999 A
5930369 Cox et al. Jul 1999 A
5930377 Powell et al. Jul 1999 A
5953049 Horn et al. Sep 1999 A
5960080 Fahlman et al. Sep 1999 A
5963909 Warren et al. Oct 1999 A
5982891 Ginter et al. Nov 1999 A
5999828 Sih et al. Dec 1999 A
6011849 Orrin Jan 2000 A
6026193 Rhoads Feb 2000 A
6035034 Trump Mar 2000 A
6052454 Kek et al. Apr 2000 A
6052462 Lu Apr 2000 A
6064963 Gainsboro May 2000 A
6072860 Kek et al. Jun 2000 A
6078567 Traill et al. Jun 2000 A
6078645 Cai et al. Jun 2000 A
6078807 Dunn et al. Jun 2000 A
6111954 Rhoads Aug 2000 A
6122392 Rhoads Sep 2000 A
6122403 Rhoads Sep 2000 A
6138119 Hall et al. Oct 2000 A
6140956 Hillman et al. Oct 2000 A
6141406 Johnson Oct 2000 A
6141415 Rao Oct 2000 A
6157707 Baulier et al. Dec 2000 A
6160903 Hamid et al. Dec 2000 A
6185416 Rudokas et al. Feb 2001 B1
6185683 Ginter et al. Feb 2001 B1
6205249 Moskowitz Mar 2001 B1
6219640 Basu et al. Apr 2001 B1
6233347 Chen et al. May 2001 B1
6237786 Ginter et al. May 2001 B1
6243480 Zhao et al. Jun 2001 B1
6243676 Witteman Jun 2001 B1
6253193 Ginter et al. Jun 2001 B1
6263507 Ahmad et al. Jul 2001 B1
6266430 Rhoads Jul 2001 B1
6278772 Bowater et al. Aug 2001 B1
6278781 Rhoads Aug 2001 B1
6289108 Rhoads Sep 2001 B1
6298122 Horne Oct 2001 B1
6301360 Bocionek et al. Oct 2001 B1
6312911 Bancroft Nov 2001 B1
6314192 Chen et al. Nov 2001 B1
6324573 Rhoads Nov 2001 B1
6324650 Ogilvie Nov 2001 B1
6327352 Betts et al. Dec 2001 B1
6330335 Rhoads Dec 2001 B1
6343138 Rhoads Jan 2002 B1
6343738 Ogilvie Feb 2002 B1
6345252 Beigi et al. Feb 2002 B1
6385548 Ananthaiyer et al. May 2002 B2
6389293 Clore et al. May 2002 B1
6421645 Beigi et al. Jul 2002 B1
6526380 Thelen et al. Feb 2003 B1
6542602 Elazar Apr 2003 B1
6549587 Li Apr 2003 B1
6584138 Neubauer et al. Jun 2003 B1
6614781 Elliott et al. Sep 2003 B1
6625587 Erten et al. Sep 2003 B1
6633846 Bennett et al. Oct 2003 B1
6647096 Brown Dec 2003 B1
6665376 Kanevsky et al. Dec 2003 B1
6665644 Haartsen Dec 2003 B1
6671292 Haartsen Dec 2003 B1
6728682 Beigi et al. Jun 2004 B2
6748356 Neumeyer et al. Jul 2004 B1
6760697 Blink Jul 2004 B1
6763099 Barak et al. Sep 2004 B1
6788772 Barak et al. Sep 2004 B2
6792030 Parker et al. Oct 2004 B2
6810480 Parker et al. Oct 2004 B1
6873617 Ahmad et al. Apr 2005 B1
6880171 Martin May 2005 B1
6895086 Parra et al. May 2005 B2
6898612 Reardon Jun 2005 B1
6907387 Reardon Jun 2005 B1
7035386 Susen et al. Apr 2006 B1
7039585 Wilmot et al. May 2006 B2
7050918 McNitt et al. Jul 2006 B2
7079636 McNitt et al. Jul 2006 B1
7079637 Bennett et al. Sep 2006 B1
7106843 Gainsboro et al. Sep 2006 B1
7103549 Martin Oct 2006 B2
7123704 Scarano et al. Nov 2006 B2
7133828 Gundla et al. Dec 2006 B2
7149788 Gundla et al. Dec 2006 B1
7197560 Martin Jul 2007 B2
7248685 Profanchik et al. Aug 2007 B2
7256816 Tian et al. Oct 2007 B2
7277468 Tian et al. Oct 2007 B2
7333798 Hodge Feb 2008 B2
7417983 He et al. Aug 2008 B2
7426265 Chen et al. Sep 2008 B2
7494061 Reinhold Feb 2009 B2
7505406 Hingoranee et al. Apr 2009 B1
7519169 Hingoranee et al. Apr 2009 B1
7522728 Rhoads Apr 2009 B1
7529357 Rae et al. May 2009 B1
7596498 Basu et al. Sep 2009 B2
7639791 Hodge Dec 2009 B2
7664243 Martin Feb 2010 B2
7765302 Whynot et al. Jul 2010 B2
7826604 Martin Nov 2010 B2
7848510 Shaffer et al. Dec 2010 B2
7853243 Hodge Dec 2010 B2
7860114 Gallant et al. Dec 2010 B1
7899167 Rae Mar 2011 B1
7916845 Polozola et al. Jun 2011 B2
7961858 Polozola et al. Jun 2011 B2
7961860 McFarlen Jun 2011 B1
8000269 Rae Aug 2011 B1
8031849 Gunasekara Nov 2011 B1
8054960 Gunasekara Nov 2011 B1
8059656 Telikepalli et al. Nov 2011 B1
8059790 Paterik et al. Nov 2011 B1
8090082 Gilbert Jan 2012 B2
8130662 Goode et al. Mar 2012 B1
8345850 Hodge Jan 2013 B2
8351581 Mikan Jan 2013 B2
8396200 Hodge et al. Mar 2013 B2
8542802 Olligschlaeger Sep 2013 B2
8630726 Hodge Jan 2014 B2
8731934 Olligschlaeger et al. May 2014 B2
8869275 Zhao et al. Oct 2014 B2
8886663 Gainsboro et al. Nov 2014 B2
8929525 Edwards Jan 2015 B1
8942356 Olligschlaeger Jan 2015 B2
8953583 Swaminathan et al. Feb 2015 B2
8953758 Kumar K.A. Feb 2015 B2
9031057 Long et al. May 2015 B2
9143610 Hodge Sep 2015 B2
9225838 Hodge et al. Dec 2015 B2
9253439 Andrada et al. Feb 2016 B2
9614974 Hodge et al. Apr 2017 B1
9621732 Olligschlaeger Apr 2017 B2
9667667 Silver et al. May 2017 B2
9930088 Hodge Mar 2018 B1
9930173 Olligschlaeger Mar 2018 B2
20010056349 St. John Dec 2001 A1
20010056461 Kampe et al. Dec 2001 A1
20020002464 Pertrushin Jan 2002 A1
20020010587 Pertrushin Jan 2002 A1
20020032566 Tzirkel-Hancock et al. Mar 2002 A1
20020184373 Maes Dec 2002 A1
20030023444 St. John Jan 2003 A1
20030040326 Levy et al. Feb 2003 A1
20030063578 Weaver Apr 2003 A1
20030076815 Miller et al. Apr 2003 A1
20030086541 Brown et al. May 2003 A1
20030086546 Falcone et al. May 2003 A1
20030088421 Maes et al. May 2003 A1
20040029564 Hodge Feb 2004 A1
20040047437 Hamiti et al. Mar 2004 A1
20040162726 Chang Aug 2004 A1
20040196867 Ejzak et al. Oct 2004 A1
20040249650 Freedman et al. Dec 2004 A1
20040252184 Hesse et al. Dec 2004 A1
20050010411 Rigazio et al. Jan 2005 A1
20050014491 Johnson Jan 2005 A1
20050060411 Coulombe et al. Mar 2005 A1
20050080625 Bennett et al. Apr 2005 A1
20050083912 Afshar et al. Apr 2005 A1
20050114192 Tor et al. May 2005 A1
20050125226 Magee Jun 2005 A1
20050128283 Bulriss et al. Jun 2005 A1
20050141694 Wengrovitz Jun 2005 A1
20050144004 Bennett et al. Jun 2005 A1
20050182628 Choi Aug 2005 A1
20050207541 Cote Sep 2005 A1
20060064037 Shalon et al. Mar 2006 A1
20060087554 Boyd et al. Apr 2006 A1
20060087555 Boyd et al. Apr 2006 A1
20060094472 Othmer et al. May 2006 A1
20060198504 Shemisa et al. Sep 2006 A1
20060200353 Bennett Sep 2006 A1
20060209794 Bae et al. Sep 2006 A1
20060285650 Hodge Dec 2006 A1
20060285665 Wasserblat et al. Dec 2006 A1
20070011235 Mutikainen et al. Jan 2007 A1
20070022289 Alt et al. Jan 2007 A1
20070047734 Frost Mar 2007 A1
20070071206 Gainsboro Mar 2007 A1
20070185717 Bennett Aug 2007 A1
20070206568 Silver et al. Sep 2007 A1
20070237099 He et al. Oct 2007 A1
20070242658 Rae Oct 2007 A1
20070244690 Peters Oct 2007 A1
20070291776 Kenrick et al. Dec 2007 A1
20080000966 Keiser Jan 2008 A1
20080021708 Bennett et al. Jan 2008 A1
20080046241 Osburn et al. Feb 2008 A1
20080106370 Perez et al. May 2008 A1
20080118045 Polozola et al. May 2008 A1
20080123687 Bangalore et al. May 2008 A1
20080195387 Zigel et al. Aug 2008 A1
20080198978 Olligschlaeger Aug 2008 A1
20080201143 Olligschlaeger et al. Aug 2008 A1
20080201158 Johnson et al. Aug 2008 A1
20080260133 Bennett Dec 2008 A1
20080300878 Da Palma et al. Dec 2008 A1
20080319761 Capuozzo et al. Dec 2008 A1
20080320148 Capuozzo et al. Dec 2008 A1
20100177881 Hodge Jul 2010 A1
20100202595 Hodge et al. Aug 2010 A1
20110055256 Phillips et al. Mar 2011 A1
20120069983 Sall Mar 2012 A1
20130007293 Den Hartog et al. Jan 2013 A1
20130163590 Tanaka et al. Aug 2013 A1
20130223304 Hori et al. Sep 2013 A1
20130230057 Suni et al. Nov 2013 A1
20130294335 Hodge et al. Dec 2013 A1
20130322614 Hodge et al. Dec 2013 A1
20140126715 Lum et al. May 2014 A1
20150078332 Sidhu et al. Mar 2015 A1
20150201083 Olligschlaeger Jul 2015 A1
20160021163 Hodge et al. Feb 2016 A1
20160044161 Hodge et al. Feb 2016 A1
20170006159 Olligschlaeger Jan 2017 A1
20170222832 Silver et al. Aug 2017 A1
Foreign Referenced Citations (8)
Number Date Country
1280137 Dec 2004 EP
2075313 Nov 1981 GB
59225626 Dec 1984 JP
60010821 Jan 1985 JP
61135239 Jun 1986 JP
3065826 Mar 1991 JP
WO 96014703 May 1996 WO
WO 98027768 Jun 1998 WO
Non-Patent Literature Citations (120)
Entry
“Audio/Video Transport (avt),” Internet Archive Wayback Machine, Oct. 16, 2002, retrieved from http://web.archive.org/web/20021016171815/http://www.ietf.org:80/html.charters/avt-charter.html.
“Cisco IAD2400 Series Business-Class Integrated Access Device”, Cisco Systems Datasheet, 2003.
“Internet Protocol DARPA Internet Program Protocol Specification,” Defense Advanced Research Projects Agency, RFC 791, Sep. 1981; 50 pages.
“Overview of the IETF,” Internet Archive Wayback Machine, Aug. 2, 2002, retrieved from http://web.archive.org/web/20020802043453/www.ietf.org/overview.html.
“SIP and IPLinkTM in the Next Generation Network: An Overview,” Intel, 2001.
“Voice Over Packet in Next Generation Networks: An Architectural Framework,” Bellcore, Special Report SR-4717, Issue 1, Jan. 1999.
“Cool Edit Pro, Version 1.2 User Guide,” Syntrillium Software Corporation, 1998.
“Criminal Calls: A Review of the Bureau of Prisons' Management of Inmate Telephone Privileges,” U.S. Department of Justice, Office of the Inspector General, Aug. 1999.
“Global Call Api for Linux and Windows Operating Systems,” Intel Dialogic Library Reference, Dec. 2005.
“The NIST Year 2002 Speaker Recognition Evaluation Plan,” NIST, Feb. 27, 2002, accessible at http://www.itl.nist.gov/iad/mig/tests/spk/2002/2002-spkrecevalplan-v60.pdf.
Andreas M. Olligschlaeger, Criminal Intelligence Databases and Applications, in Marilyn B. Peterson, Bob Morehouse, and Richard Wright, Intelligence 2000: Revising the Basic Elements—A Guide for Intelligence Professionals, 2000, a joint publications of IALEIA and LEIU, United States.
Auckenthaler, et al., “Speaker-Centric Score Normalization and Time Pattern Analysis for Continuous Speaker Verification,” International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 2, Jun. 2000, pp. 1065-1068.
Audacity Team, “About Audacity,” World Wide Web, 2014, accessible at http://wiki.audacity.team.org/wiki/About_Audacity.
Audioconferencing options. (Teleconference Units, Conference Bridges and Service Bureaus) (includes related articles on speech processing and new conferencing technology), Frankel, Elana, Teleconnect, v. 14, n. 5, p. 131(3), May 1996.
Beigi, et al., “A Hierarchical Approach to Large-Scale Speaker Recognition,” EuroSpeech 1999, Sep. 1999, vol. 5; pp. 2203-2206.
Beigi, et al., “IBM Model-Based and Frame-By-Frame Speaker-Recognition,” Speaker Recognition and its Commercial and Forensic Applications, Apr. 1998; pp. 1-4.
Beigi, H., “Challenges of Large-Scale Speaker Recognition,” 3rd European Cooperation in the Field of Scientific and Technical Research Conference, Nov. 4, 2005.
Beigi, H., “Decision Theory,” Fundamentals of Speaker Recognition, Chapter 9, Springer US, 2011; pp. 313-339.
Bender, W., et al., “Techniques for Data Hiding,” IBM Systems Journal, vol. 35, Nos. 3&4, 1996.
Black, U., Voice Over IP, Second Edition, Prentice Hall 2002; 361 pages.
Boersma, et al., “Praat: Doing Phonetics by computer,” World Wide Web, 2015, accessible at http://www.fon.hum.uva.nl/praat.
Bolton, et al., “Statistical Fraud Detection: A Review,” Statistical Science, vol. 17, No. 3 (2002), pp. 235-255.
Boney, L., et al., “Digital Watermarks for Audio Signals” Proceedings of EUSIPC0-96, Eighth European Signal processing Conference, Trieste, Italy, 10-13 (1996).
Boney, L., et al., “Digital Watermarks for Audio Signals” Proceedings of the International Conference on Multimedia Computing Systems, p. 473-480, IEEE Computer Society Press, United States (1996).
BubbleLINK® Software Architecture (Science Dynamics 2003).
Bur Goode, Voice Over Internet Protocol (VoIP), Proceedings of the IEEE, vol. 90, No. 9, 1495-1517 (Sep. 2002).
Carey, et al., “User Validation for Mobile Telephones,” International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 2, Jun. 2000, pp. 1093-1096.
Carlson, A. B., Communication Systems: an Introduction to Signals and Noise in Electrical Communication, Second Edition; pp. 15-49.
Chaudhari, et al., “Transformation enhanced multi-grained modeling for text-independent speaker recognition,” International Conference on Spoken Language Processing, 2000, pp. 298-301.
Christel, M. G., et al., “Interactive Maps for a Digital Video Library”, IEEE Special Edition on Multimedia Computing, pp. 60-67, IEEE, United States (2000).
Clavel, et al., “Events Detection for an Audio-Based Surveillance System,” IEEE International Conference on Multimedia and Expo (ICME2005), Jul. 6-8, 2005, pp. 1306-1309.
Clifford J. Weinstein, MIT, The Experimental Integrated Switched Network—A System-Level Network Test Facility (IEEE 1983).
Coherent Announces Industry's First Remote Management System for Echo Canceller, Business Wire, Mar. 3, 1997.
Commander Call Control System, Rev. 1.04 (Science Dynamics 2002).
Cox, I. J., et al.; “Secure Spread Spectrum Watermarking for Multimedia,” NEC Research Institute, Technical Report 95-10.
Defendant's Opening Claim Construction Brief, Global Tel*Link Corporation v. Securus Technologies, Inc., Case No. 3:14-cv-0829-K (N.D. Tex.), filed Nov. 19, 2014.
Defendant's Responsive Claim Construction Brief, Global Tel*Link Corporation v. Securus Technologies, Inc., Case No. 3:14-cv-0829-K (N.D. Tex.), filed Dec. 10, 2014.
Definition of “constant”, The American Heritage Dictionary, 4th Ed. (2002); p. 306.
Definition of “telephony”, McGraw-Hill Dictionary of Scientific and Technical Terms, 6th Edition (McGraw-Hill, 2003).
Definitions of “suspicion” and “suspect”, American Heritage Dictionary, 4th Edition, New York: Houghton Mifflin, 2006; pp. 1743-1744.
Doddington, G., “Speaker Recognition based on Idiolectal Differences between Speakers,” Seventh European Conference on Speech Communication and Technology, Sep. 3-7, 2001; pp. 2521-2524.
Dunn, et al., “Approaches to speaker detection and tracking in conversational speech,” Digital Signal Processing, vol. 10, 2000; pp. 92-112.
Excerpts from International Telecommunication Union, “Technical Characteristics of Tones for the Telephone Service,” ITU-T Recommendation E.180/Q.35, Mar. 9, 1998; 19 pages.
Excerpts from McGraw-Hill Dictionary of Scientific and Technical Terms, 5th Edition, 1994; pp. 680 and 1560.
Excerpts from the Prosecution History of U.S. Appl. No. 10/135,878, filed Apr. 29, 2002.
Excerpts from Webster's Third New International Dictionary, Merriam-Webster Inc., 2002, pp. 2367-2368.
File History of U.S. Pat. No. 7,899,167, U.S. Appl. No. 10/642,532, filed Aug. 15, 2003.
File History of U.S. Pat. No. 8,630,726, U.S. Appl. No. 12/378,244, filed Feb. 12, 2009.
File History of U.S. Pat. No. 8,886,663, U.S. Appl. No. 12/284,450, filed Sep. 20, 2008.
File History of U.S. Pat. No. 9,225,838, U.S. Appl. No. 13/958,137, filed Aug. 2, 2013.
Fraser et al., “Over-All Characteristics of a TASI System,” The Bell System Technical Journal, Jul. 1962; pp. 1439-1454.
Furui, et al., “Experimental studies in a new automatic speaker verification system using telephone speech,” Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '80, vol. 5, Apr. 1980, pp. 1060-1062.
Furui, S., “50 Years of Progress in Speech and Speaker Recognition Research,” ECTI Transactions on Computer and Information Technology, vol. 1, No. 2, Nov. 2005, pp. 64-74.
Greene et al., “Media Gateway Control Protocol Architecture Requirements,” Network Working Group, RFC 2805, Apr. 2000; 45 pages.
Hansen, et al., “Speaker recognition using phoneme-specific gmms,” The Speaker and Language Recognition Workshop, May-Jun. 2004.
IETF Mail Archive, Internet Archive Wayback Machine, Aug. 25, 2016, retrieved from https://mailarchive.ietf.org/archlsearchl?qRFC3389.
Inmate Telephone Services: Large Business: Voice, Oct. 2, 2001.
International Search Report and Written Opinion directed to International Patent Application No. PCT/US17/19723, dated Mar. 23, 2017; 8 pages.
International Search Report for International Application No. PCT/US04/025029, dated Mar. 14, 2006.
Isobe, et al., “A new cohort normalization using local acoustic information for speaker verification,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, Mar. 1999; pp. 841-844.
Jeff Hewett and Lee Dryburgh, Signaling System No. 7 (SS7/C7): Protocol, Architecture, and Services (Networking Technology) at 85 (Cisco Press, Jun. 2005).
Johnston, et al., “Session Initiation Protocol Services Examples,” Best Current Practice, RFC 5359, Oct. 2008, pp. 1-170.
Juang, et al., “Automatic Speech Recognition—A Brief History of the Technology Development,” Oct. 8, 2014.
Kinnunen, et al., “Real-Time Speaker Identification and Verification,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, No. 1, Jan. 2006, pp. 277-288.
Knox, “The Problem of Gangs and Security Threat Groups (STG's) in American Prisons Today: Recent Research Findings From the 2004 Prison Gang Survey,” National Gang Crime Research Center, 2005; 67 pages.
Lane, I. R., et al., “Language Model Switching Based on Topic Detection for Dialog Speech Recognition,” Proceedings of the IEEE-ICASSP, vol. 1, pp. 616-619, IEEE, United States (2003).
Maes, et al., “Conversational speech biometrics,” E-Commerce Agents, Marketplace Solutions, Security Issues, and Supply and Demand, Springer-Verlang, London, UK, 2001, pp. 166-179.
Maes, et al., “Open SESAME! Speech, Password or Key to Secure Your Door?,” Asian Conference on Computer Vision, Jan. 1998; pp. 1-3.
Matsui, et al., “Concatenated Phoneme Models for Text-Variable Speaker Recognition,” International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 2, Apr. 1993; pp. 391-394.
Moattar, et al., “Speech Overlap Detection Using Spectral Features and its Application in Speech Indexing,” Second International Conference on Information & Communication Technologies, 2006; 5 pages.
National Alliance of Gang Investigators Associations, 2005 National Gang Threat Assessment, 2005, pp. vi and 5-7, Bureau of Justice Assistance, Office of Justice Programs, U.S. Department of Justice.
National Major Gang Task Force, A Study of Gangs and Security Threat Groups in America's Adult Prisons and Jails, 2002, United States.
Navratil, et al., “A Speech Biometrics System with Multi-Grained Speaker Modeling,” Proceedings of KOVENS 2000; 5 pages.
Navratil, et al., “Phonetic Speaker Recognition using Maximum-Likelihood Binary-Decision Tree Models,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Apr. 6-10, 2003; 4 pages.
Newton's Telecom Dictionary, 18th Edition, Feb. 2002, p. 168, section “coding theory”.
Office Action dated Dec. 1, 2011, in Canadian Patent Application No. 2,534,767, DSI-ITI, LLC, filed Aug. 4, 2004.
Olligschlaeger, A. M., “Criminal Intelligence Databases and Applications”, in Marilyn B. Peterson, Bob Morehouse, and Richard Wright, Intelligence 2000: Revising the Basic Elements—A Guide for Intelligence Professionals, 2000, a joint publication of IALEIA and LEIU, United States.
Original Specification as-filed Aug. 26, 2005, in U.S. Appl. No. 11/212,495 to Frost.
Original Specification as-filed Jul. 22, 2005, in U.S. Appl. No. 11/187,423 to Shaffer.
Osifchin, N., “A Telecommunications Buildings/Power Infrastructure in a New Era of Public Networking,” IEEE 2000.
PacketCableTM 1.0 Architecture Framework Technical Report, PKT-TR-ARCH-V0 1-001201 (Cable Television Laboratories, Inc. 1999).
Pages from http://www.corp.att.com/history, archived by web.archive.org on Nov. 4, 2013.
Pelecanos, J. “Conversational biometrics,” in Biometric Consortium Meeting, Baltimore, MD, Sep. 2006, accessible at http://www.biometrics.org/bc2006/presentations/Thu_Sep_21/Session_I/Pelecanos_Conversational_Biometrics.pcif.
Perkins, C., RTP Audio and Video for the Internet, Pearson Education, 2003.
Pfaffenberger, B., Webster's New World Dictionary of Computer Terms, Eighth Edition, 2000; p. 22.
Photocopy of “Bellcore Notes on the Networks (Formerly BOC Notes on the LEC Networks),” Bellcore, Special Report SR-2275, Issue 3, Dec. 1997.
Pollack, et al., “On the identification of Speakers by Voice,” The Journal of the Acoustical Society of America, vol. 26, No. 3, May 1954.
Postel, J., “User Datagram Protocol,” ISI, RFC 768, Aug. 28, 1980; 3 pages.
Proakis, John G., Digital Communications, Second Edition, McGraw-Hill, Inc. 1989; pp. 148-157.
Prosecution History of U.S. Appl. No. 10/910,566, filed Aug. 4, 2004.
Prosecution History of U.S. Appl. No. 11/480,258, filed Jun. 30, 2006.
Prosecution History of U.S. Appl. No. 12/002,507, filed Dec. 17, 2007.
Rey, R.F., ed., “Engineering and Operations in the Bell System,” 2nd Edition, AT&T Bell Laboratories: Murray Hill, NJ, 1983.
Reynolds, D., “Automatic Speaker Recognition Using Gaussian Mixture Speaker Models,” The Lincoln Laboratory Journal, vol. 8, No. 2, 1995; pp. 173-192.
Rosenberg et al., “SIP: Session Initiation Protocol,” Network Working Group, Standards Track, RFC 3261, Jun. 2002, 269 pages.
Rosenberg, et al., “The Use of Cohort Normalized Scores for Speaker Verification,” Speech Research Department, AT&T Bell Laboratories, 2nd International Conference on Spoken Language Processing, Banff, Alberta, Canada, Oct. 12-16, 1992.
Ross, et al., “Multimodal Biometrics: An Overview,” Proc. of 12th European Signal Processing Conference (EUSIPCO), Vienna, Austria, Sep. 2004, pp. 1221-1224.
Russell, T., Signaling System #7, Fourth Edition, McGraw-Hill, 2002; 532 pages.
Schulzrinne et al., “RTP: A Transport Protocol for Real-Time Applications,” Network Working Group, RFC 3550, Jul. 2003; 89 pages.
Science Dynamics, Inmate Telephone Control Systems, http://scidyn.com/fmudprev_main.htm (archived by web.archive.org on Jan. 12, 2001).
Science Dynamics, SciDyn BubbleLINK, http://www.scidyn.com/products/bubble.html (archived by web.archive.org on Jun. 18, 2006).
Science Dynamics, SciDyn Call Control Solutions: Commander II, http://www.scidyn.com/products/commander2.html (archived by web.archive.org on Jun. 18, 2006).
Science Dynamics, SciDyn IP Gateways, http://scidyn.com/products/ipgateways.html (archived by web.archive.org on Aug. 15, 2001).
Science Dynamics, Science Dynamics—IP Telephony, http://www.scidyn.com/iptelephony_maim.htm (archived by web.archive.org on Oct. 12, 2000).
Shearme, et al., “An Experiment Concerning the Recognition of Voices,” Language and Speech, vol. 2, No. 3, Jul./Sep. 1959.
Silberg, L. “Digital on Call,” HFN The Weekly Newspaper for the Home Furnishing Network, p. 97, Mar. 17, 1997.
Statement for the Record of John S. Pistole, Assistant Director, Counterterrorism Division, Federal Bureau of Investigation, Before the Senate Judiciary Committee, Subcommittee on Terrorism, Technology, and Homeland Security, Oct. 14, 2003.
Supplementary European Search Report for EP Application No. EP 04 80 9530, Munich, Germany, completed on Mar. 25, 2009.
Tirkel, A., et al.; “Image Watermarking—A Spread Spectrum Application.”
U.S. Appl. No. 60/607,447, “IP-based telephony system and method,” to Apple, et al., filed Sep. 3, 2004.
Viswanathan, et al., “Multimedia Document Retrieval using Speech and Speaker Recognition,” International Journal on Document Analysis and Recognition, Jun. 2000, vol. 2; pp. 1-24.
Weisstein, Eric W., “Average Power,” MathWorld—A Wolfram Web Resource, 1999, retrieved from http://mathworld.wolfram.com/AveragePower.html.
Wozencraft et al., Principles of Communication Engineering, 1965; pp. 233-245.
Zajic, et al., “A Cohort Methods for Score Normalization in Speaker Verification Systme, Acceleration of On-Line Cohort Methods,” Proceedings of the 12th International Conference “Speech and Computer,” Oct. 15-18, 2007; 6 pages.
Zopf, R., “Real-time Transport Protocol (RTP) Payload for Comfort Noise,” Network Working Group RFC 3389, Sep. 2002; 8 pages.
Blackwell, “The Time Factor in Telephone Transmission”, Bell System Technical Journal, 1932; pp. 53-66.
File History of U.S. Pat. No. 7,123,704, U.S. Appl. No. 11/035,071, filed Jan. 14, 2005.
Goodwin, “CATV Tap and Splitter Linearity Improvement for Broadband Information Networks,” 31 ARFTG 1997; pp. 34-38.
Habetler, “Acoustic Noise Reduction in Sinusoidal PWM Drives Using a Randomly Modulated Carrier,” IEEE Transactions on Power Electronics, vol. 6, No. 3, Jul. 1991; pp. 356-363.
Horowitz et al., The Art of Electronics, Second Edition, 1994.
Related Publications (1)
Number Date Country
20180219997 A1 Aug 2018 US
Continuations (4)
Number Date Country
Parent 15265510 Sep 2016 US
Child 15937269 US
Parent 14604388 Jan 2015 US
Child 15265510 US
Parent 13971292 Aug 2013 US
Child 14604388 US
Parent 11706431 Feb 2007 US
Child 13971292 US