Patent Grant
9691376
The Field of the Invention
The invention relates to methods and apparatus for synthesizing speech.
Description of Related Art
Rule-based speech synthesis is used for various types of speech synthesis applications including Text-To-Speech (TTS) and voice response systems. Typical rule-based speech synthesis techniques involve concatenating pre-recorded phonemes to form new words and sentences.
Previous concatenative speech synthesis systems create synthesized speech by using single stored samples for each phoneme in order to synthesize a phonetic sequence. A phoneme, or phone, is a small unit of speech sound that serves to distinguish one utterance from another. For example, in the English language, the phoneme /r/ corresponds to the letter “R” while the phoneme /t/ corresponds to the letter “T”. Synthesized speech created by this technique sounds unnatural and is usually characterized as “robotic” or “mechanical.”
More recently, speech synthesis systems started using large inventories of acoustic units with many acoustic units representing variations of each phoneme. An acoustic unit is a particular instance, or realization, of a phoneme. Large numbers of acoustic units can all correspond to a single phoneme, each acoustic unit differing from one another in terms of pitch, duration, and stress as well as various other qualities. While such systems produce a more natural sounding voice quality, to do so they require a great deal of computational resources during operation. Accordingly, there is a need for new methods and apparatus to provide natural voice quality in synthetic speech while reducing the computational requirements.
The invention provides methods and apparatus for speech synthesis by selecting recorded speech fragments, or acoustic units, from an acoustic unit database. To aide acoustic unit selection, a measure of the mismatch between pairs of acoustic units, or concatenation cost, is pre-computed and stored in a database. By using a concatenation cost database, great reductions in computational load are obtained compared to computing concatenation costs at run-time.
The concatenation cost database can contain the concatenation costs for a subset of all possible acoustic unit sequential pairs. Given that only a fraction of all possible concatenation costs are provided in the database, the situation can arise where the concatenation cost for a particular sequential pair of acoustic units is not found in the concatenation cost database. In such instances, either a default value is assigned to the sequential pair of acoustic units or the actual concatenation cost is derived.
The concatenation cost database can be derived using statistical techniques which predict the acoustic unit sequential pairs most likely to occur in common speech. The invention provides a method for constructing a medium with an efficient concatenation cost database by synthesizing a large body of speech, identifying the acoustic unit sequential pairs generated and their respective concatenation costs, and storing the concatenation costs values on the medium.
Other features and advantages of the present invention will be described below or will become apparent from the accompanying drawings and from the detailed description which follows.
The invention is described in detail with regard to the following figures, wherein like numerals reference like elements, and wherein:
The data source 102 can provide the text-to-speech synthesizer 104 with data which represents the text to be synthesized into speech via the input link 108. The data representing the text of the speech to be synthesized can be in any format, such as binary, ASCII or a word processing file. The data source 102 can be any one of a number of different types of data sources, such as a computer, a storage device, or any combination of software and hardware capable of generating, relaying, or recalling from storage a textual message or any information capable of being translated into speech.
The data sink 106 receives the synthesized speech from the text-to-speech synthesizer 104 via the output link 110. The data sink 106 can be any device capable of audibly outputting speech, such as a speaker system capable of transmitting mechanical sound waves, or it can be a digital computer, or any combination of hardware and software capable of receiving, relaying, storing, sensing or perceiving speech sound or information representing speech sounds.
The links 108 and 110 can be any known or later developed device or system for connecting the data source 102 or the data sink 106 to the text-to-speech synthesizer 104. Such devices include a direct serial/parallel cable connection, a connection over a wide area network or a local area network, a connection over an intranet, a connection over the Internet, or a connection over any other distributed processing network or system. Additionally, the input link 108 or the output link 110 can be software devices linking various software systems. In general, the links 108 and 110 can be any known or later developed connection system, computer program, or structure useable to connect the data source 102 or the data sink 106 to the text-to-speech synthesizer 104.
In operation, textual data can be received from an external data source 102 using the input link 108. The text normalization device 202 can receive the text data in any readable format, such as an ASCII format. The text normalization device can then parse the text data into known words and further convert abbreviations and numbers into words to produce a corresponding set of normalized textual data. Text normalization can be done by using an electronic dictionary, database or informational system now known or later developed without departing from the spirit and scope of the present invention.
The text normalization device 202 then transmits the corresponding normalized textual data to the linguistic analysis device 204 via the data bus 212. The linguistic analysis device 204 can translate the normalized textual data into a format consistent with a common stream of conscious human thought. For example, the text string “$10”, instead of being translated as “dollar ten”, would be translated by the linguistic analysis unit 11 as “ten dollars.” Linguistic analysis devices and methods are well known to those skilled in the art and any combination of hardware, software, firmware, heuristic techniques, databases, or any other apparatus or method that performs linguistic analysis now known or later developed can be used without departing from the spirit and scope of the present invention.
The output of the linguistic analysis device 204 can be a stream of phonemes. A phoneme, or phone, is a small unit of speech sound that serves to distinguish one utterance from another. The term phone can also refer to different classes of utterances such as poly-phonemes and segments of phonemes such as half-phones. For example, in the English language, the phoneme /r/ corresponds to the letter “R” while the phoneme /t/ corresponds to the letter “T”. Furthermore, the phoneme /r/ can be divided into two half-phones /rl/ and /rr/which together could represent the letter “R”. However, simply knowing what the phoneme corresponds to is often not enough for speech synthesizing because each phoneme can represent numerous sounds depending upon its context.
Accordingly, the stream of phonemes can be further processed by the prosody generation device 206 which can receive and process the phoneme data stream to attach a number of characteristic parameters describing the prosody of the desired speech. Prosody refers to the metrical structure of verse. Humans naturally employ prosodic qualities in their speech such as vocal rhythm, inflection, duration, accent and patterns of stress. A “robotic” voice, on the other hand, is an example of a non-prosodic voice. Therefore, to make synthesized speech sound more natural, as well as understandable, prosody must be incorporated.
Prosody can be generated in various ways including assigning an artificial accent or providing for sentence context. For example, the phrase “This is a test!” will be spoken differently from “This is a test?” Prosody generating devices and methods are well known to those of ordinary skill in the art and any combination of hardware, software, firmware, heuristic techniques, databases, or any other apparatus or method that performs prosody generation now known or later developed can be used without departing from the spirit and scope of the invention.
The phoneme data along with the corresponding characteristic parameters can then be sent to the acoustic unit selection device 208 where the phonemes and characteristic parameters can be transformed into a stream of acoustic units that represent speech. An acoustic unit is a particular utterance of a phoneme. Large numbers of acoustic units can all correspond to a single phoneme, each acoustic unit differing from one another in terms of pitch, duration, and stress as well as various other phonetic or prosodic qualities. Subsequently, the acoustic unit stream can be sent to the speech synthesis back end device 210 which converts the acoustic unit stream into speech data and can transmit the speech data to a data sink 106 over the output link 110.
In operation, and under the control of the controller 302, the input interface 312 can receive the phoneme data along with the corresponding characteristic parameters for each phoneme which represent the original text data. The input interface 312 can receive input data from any device, such as a keyboard, scanner, disc drive, a UART, LAN, WAN, parallel digital interface, software interface or any combination of software and hardware in any form now known or later developed. Once the controller 302 imports a phoneme stream with its characteristic parameters, the controller 302 can store the data in the system memory 316.
The controller 302 then assigns groups of acoustic units to each phoneme using the acoustic unit database 306. The acoustic unit database 306 contains recorded sound fragments, or acoustic units, which correspond to the different phonemes. In order to produce a very high quality of speech, the acoustic unit database 306 can be of substantial size wherein each phoneme can be represented by hundreds or even thousands of individual acoustic units. The acoustic units can be stored in the form of digitized speech. However, it is possible to store the acoustic units in the database in the form of Linear Predictive Coding (LPC) parameters, Fourier representations, wavelets, compressed data or in any form now known or later discovered.
Next, the controller 302 accesses the concatenation cost database 310 using the hash table 308 and assigns concatenation costs between every sequential pair of acoustic units. The concatenation cost database 310 of the exemplary embodiment contains the concatenation costs of a subset of the possible acoustic unit sequential pairs. Concatenation costs are measures of mismatch between two acoustic units that are sequentially ordered. By incorporating and referencing a database of concatenation costs, run-time computation is substantially lower compared to computing concatenation costs during run-time. Unfortunately, a complete concatenation cost database can be inconveniently large. However, a well-chosen subset of concatenation costs can constitute the database 310 with little effect on speech quality.
After the concatenation costs are computed or assigned, the controller 302 can select the sequence of acoustic units that best represents the phoneme stream based on the concatenation costs and any other cost function relevant to speech synthesis. The controller then exports the selected sequence of acoustic units via the output interface 314.
While it is preferred that the acoustic unit database 306, the concatenation cost database 310, the hash table 308 and the system memory 314 in
The output interface 314 is used to output acoustic information either in sound form or any information form that can represent sound. Like the input interface 312, the output interface 314 should not be construed to refer exclusively to hardware, but can be any known or later discovered combination of hardware and software routines capable of communicating or storing data.
The example of
Once the data structure of phonemes and acoustic units is established, acoustic unit selection begins by searching the data structure for the least cost path between all acoustic units 432 taking into account the various cost functions, i.e., the target costs 432 and the concatenation costs 430. The controller 302 selects acoustic units 432 using a Viterbi search technique formulated with two cost functions: (1) the target cost 434 mentioned above, defined between each acoustic unit 432 and respective phone 404-410, and (2) concatenation costs (join costs) 430 defined between each acoustic unit sequential pair.
Additionally, the phoneme tr(1) in the second acoustic unit group 416 can be sequentially joined by any one of the phonemes uwl(1), uwl(2) and uwl(3) in the third acoustic unit group 418 to form three separate sequential acoustic unit pairs, tr(1)-uwl(1), tr(1)-uwl(2) and tr(1)-uwl(3). Connecting each sequential pair of acoustic units is a separate concatenation cost 430, each represented by an arrow.
The concatenation costs 430 are estimates of the acoustic mismatch between two acoustic units. The purpose of using concatenation costs 430 is to smoothly join acoustic units using as little processing as possible. The greater the acoustic mismatch between two acoustic units, the more signal processing must be done to eliminate the discontinuities. Such discontinuities create noticeable “pops” and “clicks” in the synthesized speech that impairs the intelligibility and quality of the resulting synthesized speech. While signal processing can eliminate much or all of the discontinuity between two acoustic units, the run-time processing decreases and synthesized speech quality improves with reduced discontinuities.
A target costs 434, as mentioned above, is an estimate of the mismatch between a recorded acoustic unit and the specification of each phoneme. The target costs 434 function is to aide in choosing appropriate acoustic units, i.e., a good fit to the specification that will require little or no signal processing. Target costs Ct for a phone specification Land acoustic unit ui is the weighted sum of target subcosts Ctj; across the phones j from 1 to p. Target costs Ct can be represented by the equation:
where p is the total number of phones in the phoneme stream.
For example, the target costs 434 for the acoustic unit tr(1) and the phoneme kr/ 406 with its associated characteristics can be fifteen (15) while the target cost 434 for the acoustic unit tr(2) can be ten (10). In this example, the acoustic unit t1(2) will require less processing than tr(1) and therefore tr(2) represents a better fit to phoneme kr/.
The concatenation cost Cc for acoustic units ui-l and ui is the weighted sum of subcosts Ccj across phones j from 1 to p. Concatenation costs can be represented by the equation:
where p is the total number of phones in the phoneme stream
For example, assume that the concatenation cost 430 between the acoustic unit tr(3) and uwl(1) is twenty (20) while the concatenation cost 430 between tr(3) and uwl(2) is ten (10) and the concatenation cost 430 between acoustic unit tr(3) and uwl(3) is zero. In this example, the transition tr(3)-uwl(2) provides a better fit than tr(3)-uwl(1), thus requiring less processing to smoothly join them. However, the transition tr(3)-uwl(3) provides the smoothest transition of the three candidates and the zero concatenation cost 430 indicates that no processing is required to join the acoustic unit sequential pairs tr(3)-uwl(3).
The task of acoustic unit selection then is finding acoustic units ui from the recorded inventory of acoustic units 306 that minimize the sum of these two costs 430 and 434, accumulated across all phones i in an utterance. The task can be represented by the following equation:
where p is the total number of phones in a phoneme stream.
A Viterbi search can be used to minimize Ct(ti,ui) by determining the least cost path that minimizes the sum of the target costs 434 and concatenation costs 430 for a phoneme stream with a given set of phonetic and prosodic characteristics.
Next, in step 504, groups of acoustic units are assigned to each phoneme in the phoneme stream. Again, referring to
The process then proceeds to step 506, where the target costs 434 are computed between each acoustic unit 432 and a corresponding phoneme with assigned characteristic parameters. Next, in step 508, concatenation costs 430 between each acoustic unit 432 and every acoustic unit 432 in a subsequent set of acoustic units are assigned.
In step 510, a Viterbi search determines the least cost path of target costs 434 and concatenation costs 430 across all the acoustic units in the data stream. While a Viterbi search is the preferred technique to select the most appropriate acoustic units 432, any technique now known or later developed suited to optimize or approximate an optimal solution to choose acoustic units 432 using any combination of target costs 434, concatenation costs 430, or any other cost function can be used without deviating from the spirit and scope of the present invention.
Next, in step 512, acoustic units are selected according to the criteria of step 510.
The speech synthesis technique of the present example is the Harmonic Plus Noise Model (HNM). The details of the HNM speech synthesis back-end are more fully described in Beutnagel, Mohri, and Riley, “Rapid Unit Selection from a large Speech Corpus for Concatenative Speech Synthesis” and Y. Stylianou (1998) “Concatenative speech synthesis using a Harmonic plus Noise Model”, Workshop on Speech Synthesis, Jenolan Caves, NSW, Australia, November 1998, incorporated herein by reference.
While the exemplary embodiment uses the HNM approach to synthesize speech, the HNM approach is but one of many viable speech synthesis techniques that can be used without departing from the spirit and scope of the present invention. Other possible speech synthesis techniques include, but are not limited to, simple concatenation of unmodified speech units, Pitch-Synchronous OverLap and Add (PSOLA), Waveform-Synchronous OverLap and Add (WSOLA), Linear Predictive Coding (LPC), Multipulse LPC, Pitch-Synchronous Residual Excited Linear Prediction (PSRELP) and the like.
As discussed above, to reduce run-time computation, the exemplary embodiment employs the concatenation cost database 310 so that computing concatenation costs at run-time can be avoided. Also as noted above, a drawback to using a concatenation cost database 310 as opposed to computing concatenation costs is the large memory requirements that arise. In the exemplary embodiment, the acoustic library consists of a corpus of eighty-four thousand (84,000) half-units (42,000 left-half and 42,000 right-half units) and, thus, the size of a concatenation cost database 310 becomes prohibitive considering the number of possible transitions. In fact, this exemplary embodiment yields 1.76 billion possible combinations. Given the large number of possible combinations, storing of the entire set of concatenation costs becomes prohibitive. Accordingly, the concatenation cost database 310 must be reduced to a manageable size.
One technique to reduce the concatenation cost database 310 size is to first eliminate some of the available acoustic units 432 or “prune” the acoustic unit database 306. One possible method of pruning would be to synthesize a large body of text and eliminate those acoustic units 432 that rarely occurred. However, experiments reveal that synthesizing a large test body of text resulted in about 85% usage of the eighty-four thousand (84,000) acoustic units in a half-phone based synthesizer. Therefore, while still a viable alternative, pruning any significant percentage of acoustic units 432 can result in a degradation of the quality of speech synthesis.
A second method to reduce the size of the concatenation cost database 310 is to eliminate from the database 310 those acoustic unit sequential pairs that are unlikely to occur naturally. As shown earlier, the present embodiment can yield 1.76 billion possible combinations. However, since experiments show the great majority of sequences seldom, if ever, occur naturally, the concatenation cost database 310 can be substantially reduced without speech degradation. The concatenation cost database 310 of the example can contains concatenation costs 430 for a subset of less than 1% of the possible acoustic unit sequential pairs.
Given that the concatenation cost database 310 only includes a fraction of the total concatenation costs 430, the situation can arise where the concatenation cost 430 for an incident acoustic sequential pair does not reside in the database 310. These occurrences represent acoustic unit sequential pairs that occur but rarely in natural speech, or the speech is better represented by other acoustic unit combinations or that are arbitrarily requested by a user who enters it manually. Regardless, the system should be able to process any phonetic input.
In step 606, a determination is made as to whether the concatenation cost 430 for the immediate acoustic unit sequential pair appears in the database 310. If the concatenation cost 430 for the immediate sequential pair appears in the concatenation cost database 310, step 610 is performed; otherwise step 608 is performed.
In step 610, because the concatenation cost 430 for the immediate sequential pair is in the concatenation cost database 310, the concatenation cost 430 is extracted from the concatenation cost database 310 and assigned to the acoustic unit sequential pair.
In contrast, in step 608, because the concatenation cost 430 for the immediate sequential pair is absent from the concatenation cost database 310, a large default concatenation cost is assigned to the acoustic unit sequential pair. The large default cost should be sufficient to eliminate the join under any reasonable circumstances (such as reasonable pruning), but not so large as to totally preclude the sequence of acoustic units entirely. It can be possible that situations will arise in which the Viterbi search must consider only two sets of acoustic unit sequences for which there are no cached concatenation costs. Unit selection must continue based on the default concatenation costs and must select one of the sequences. The fact that all the concatenation costs are the same is mitigated by the target costs, which do still vary and provide a means to distinguish better candidates from worse.
Alternatively to the default assignment of step 608, the actual concatenation cost can be computed. However, an absence from the concatenation cost database 310 indicates that the transition is unlikely to be chosen.
In step 704, the selected text is synthesized using a speech synthesizer. Next, in step 706, the occurrence of each acoustic unit 432 synthesized in step 704 is logged along with the concatenation costs 430 for each acoustic unit sequential pair. In the exemplary embodiment, the AP newswire stories selected produced approximately two hundred and fifty thousand (250,000) sentences containing forty-eight (48) million half-phones and logged a total of fifty (50) million non-unique acoustic unit sequential pairs representing a mere 1.2 million unique acoustic unit sequential pairs.
In step 708, a set of acoustic unit sequential pairs and their associated concatenation costs 430 are selected. The set chosen can incorporate every unique acoustic sequential pair observed or any subset thereof without deviating from the spirit and scope of the present invention.
Alternatively, the acoustic unit sequential pairs and their associated concatenation costs 430 can be formed by any selection method, such as selecting only acoustic unit sequential pairs that are relatively inexpensive to concatenate, or join. Any selection method based on empirical or theoretical advantage can be used without deviating from the spirit and scope of the present invention.
In the exemplary embodiment, subsequent tests using a separate set of eight thousand (8000) AP sentences produced 1.5 million non-unique acoustic unit sequential pairs, 99% of which were present in the training set. The tests and subsequent results are more fully described in Beutnagel, Mohri, and Riley, “Rapid Unit Selection from a large Speech Corpus for Concatenative Speech Synthesis,” Proc. European Conference on Speech, Communication and Technology (Eurospeech), Budapest, Hungary (September 1999) incorporated herein by reference. Experiments show that by caching 0.7% of the possible joins, 99% of join cost are covered with a default concatenation cost being otherwise substituted.
In step 710, a concatenation cost database 310 is created to incorporate the concatenation costs 430 selected in step 708. In the exemplary embodiment, based on the above statistics, a concatenation cost database 310 can be constructed to incorporate concatenation costs 430 for about 1.2 million acoustic unit sequential pairs.
Next, in step 712, a hash table 308 is created for quick referencing of the concatenation cost database 310 and the process ends with step 714. A hash table 308 provides a more compact representation given that the values used are very sparse compared to the total search space. In the present example, the hash function maps two unit numbers to a hash table 308 entry containing the concatenation costs plus some additional information to provide quick look-up.
To further improve performance and avoid the overhead associated with the general hashing routines, the present example implements a perfect hashing scheme such that membership queries can be performed in constant time. The perfect hashing technique of the exemplary embodiment is presented in detail below and is a refinement and extension of the technique presented by Robert Endre Tarj an and Andrew Chi-Chih Yao, “Storing a Sparse Table”, Communications of the ACM, vol. 22:11, pp. 606-11, 1979, incorporated herein by reference. However, any technique to access membership to the concatenation cost database 310, including non-perfect hashing systems, indices, tables, or any other means now known or later developed can be used without deviating from the spirit and scope of the invention.
The above-detailed invention produces a very natural and intelligible synthesized speech by providing a large database of acoustical units while drastically reducing the computer overhead needed to produce the speech.
It is important to note that the invention can also operate on systems that do not necessarily derive their information from text. For example, the invention can derive original speech from a computer designed to respond to voice commands.
The invention can also be used in a digital recorder that records a speaker's voice, stores the speaker's voice, then later reconstructs the previously recorded speech using the acoustic unit selection system 208 and speech synthesis back-end 210.
Another use of the invention can be to transmit a speaker's voice to another point wherein a stream of speech can be converted to some intermediate form, transmitted to a second point, then reconstructed using the acoustic unit selection system 208 and speech synthesis back-end 210.
Another embodiment of the invention can be a voice disguising method and apparatus. Here, the acoustic unit selection technique uses an acoustic unit database 306 derived from an arbitrary person or target speaker. A speaker providing the original speech, or originating speaker, can provide a stream of speech to the apparatus wherein the apparatus can reconstruct the speech stream in the sampled voice of the target speaker. The transformed speech can contain all or most of the subtleties, nuances, and inflections of the originating speaker, yet take on the spectral qualities of the target speaker.
Yet another example of an embodiment of the invention would be to produce synthetic speech representing non-speaking objects, animals or cartoon characters with reduced reliance on signal processing. Here the acoustic unit database 306 would comprise elements or sound samples derived from target speakers such as birds, animals or cartoon characters. A stream of speech entered into an acoustic unit selection system 208 with such an acoustic unit database 306 can produce synthetic speech with the spectral qualities of the target speaker, yet can maintain subtleties, nuisances, and inflections of an originating speaker.
As shown in
The exemplary technique for forming the hash table described above is a refinement and extension of the hashing technique presented by Tarj an and Yao. It consists of compacting a matrix-representation of an automaton with state set Q and transition set E by taking advantages of its sparseness, while using a threshold θ to accelerate the construction of the table.
The technique constructs a compact one-dimensional array “C” with two fields: “label” and “next.” Assume that the current position in the array is “k”, and that an input label “1” is read. Then that label is accepted by the automaton if label[C[k+1]]=1 and, in that case, the current position in the array becomes next[C[k+l]].
These are exactly the operations needed for each table look-up. Thus, the technique is also nearly optimal because of the very small number of elementary operations it requires. In the exemplary embodiment, only three additions and one equality test are needed for each look-up.
The pseudo-code of the technique is given below. For each state qεQ, E[q] represents the set of outgoing transitions of “Q.” For each transition eεE, i[e] denotes the input label of that transmission, n[e] its destination state.
The technique maintains a Boolean array “empty”, such that empty[e]=FALSE when position “k” of array “C” is non-empty. Lines 1-3 initialize array “C” by setting all labels to UNDEFINED, and initialize array “empty” to TRUE for all indices.
The loop of lines 5-21 is executed |Q| times. Each iteration of the loop determines the position pos[q] of the state “q” (or the row of index “q”) in the array “C” and inserts the transitions leaving “q” at the appropriate positions. The original position to the row is 0 (line 6). The position is then shifted until it does not coincide with that of a row considered in previous iterations (lines 7-13).
Lines 14-17 check if there exists an overlap with the row previously considered. If there is an overlap, the position of the row is shifted by one and the steps of lines 5-12 are repeated until a suitable position is found for the row of index “q.” That position is marked as non-empty using array “empty”, and as final when “q” is a final state. Non-empty elements of the row (transitions leaving q) are then inserted in the array “C” (lines 16-18). Array “pos” is used to determine the position of each state in the array “C”, and thus the corresponding transitions.
A variable “wait” keeps track of the number of unsuccessful attempts when trying to find an empty slot for a state (line 8). When that number goes beyond a predefined waiting threshold θ (line 9), “step” calls are skipped to accelerate the technique (line 12), and the present position is stored in variable “m” (line 11). The next search for a suitable position will start at “m” (line 6), thereby saving the time needed to test the first cells of array “C”, which quickly becomes very dense.
Array “pos” gives the position of each state in the table “C”. That information can be encoded in the array “C” if attribute “next” is modified to give the position of the next state pos[q] in the array “C” instead of its number “q”. This modification is done at lines 22-24.
While this invention has been described in conjunction with the specific embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art. Accordingly, preferred embodiments of the invention as set forth herein are intended to be illustrative, not limiting. Accordingly, there are changes that can be made without departing from the spirit and scope of the invention.
The present application is a continuation of U.S. patent application Ser. No. 14/335,302, filed Jul. 18, 2014, now U.S. Pat. No. 9,236,044, issued Jan. 12, 2016, which is a continuation of U.S. patent application Ser. No. 13/680,622, filed Nov. 19, 2012, now U.S. Pat. No. 8,788,268, issued Jul. 22, 2014, which is a continuation of U.S. patent application Ser. No. 13/306,157, filed Nov. 29, 2011, now U.S. Pat. No. 8,315,872, issued Nov. 20, 2012, which is a continuation of U.S. patent application Ser. No. 12/839,937, filed Jul. 20, 2010, now U.S. Pat. No. 8,086,456, issued Dec. 27, 2011, which is a continuation of U.S. application Ser. No. 12/057,020, filed Mar. 27, 2008, now U.S. Pat. No. 7,761,299, issued Jul. 20, 2010, which is a continuation of U.S. patent application Ser. No. 11/381,544, filed on May 4, 2006, now U.S. Pat. No. 7,369,994, issued May 6, 2008, which is a continuation of U.S. patent application Ser. No. 10/742,274, filed on Dec. 19, 2003, now U.S. Pat. No. 7,082,396, issued Jul. 25, 2006, which is a continuation of U.S. patent application Ser. No. 10/359,171, filed on Feb. 6, 2003, now U.S. Pat. No. 6,701,295, issued Mar. 2, 2004, which is a continuation of U.S. patent application Ser. No. 09/557,146, filed on Apr. 25, 2000, now U.S. Pat. No. 6,697,780, issued Feb. 24, 2004, which claims the benefit of U.S. Provisional Application No. 60/131,948, filed on Apr. 30, 1999. Each of these patent applications is incorporated herein by reference in its entirety.
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