Patent Grant
7660713
The present invention generally relates to signal processing, and more particularly to systems and methods that employ a linear discriminative estimator to determine whether a desired signal is present within a received signal.
Signal detection is an important element in many applications. Examples of such applications include, but are not restricted to, speech detection, speech recognition, speech coding, noise adaptation, speech enhancement, microphone arrays and echo-cancellation. In some instances, a simple frame level decision (e.g., yes or no) of whether a desired signal is present or absent is sufficient for the application. However, even with simple decisions, decision-making criteria or requirements can vary from application to application and/or for an application, based on current circumstances. For example, with source localization, it is typically important to employ a system that mitigates rendering false positives or false detections (classifying a noise-only frame as a speech frame), whereas in speech coding a high speech detection rate (e.g., rendering true positives) at the cost of an increased number of false positives commonly is acceptable and desirable.
In other instances, a simple determination of whether a desired signal is present or absent is insufficient. With these applications, it is often necessary to estimate a probability of the presence of speech in one or more frames and/or associated time-frequency bins (atoms, units). A threshold can be defined and utilized in connection with the estimated probability to facilitate deciding whether the desired signal is present. An ideal system is one that generates calibrated probabilities that accurately reflect the actual frequency of occurrence of the event (e.g., presence of a desired signal). Such a system can optimally make decisions based on utility theory and combine decisions from independent sources utilizing simple rules. Furthermore, the ideal system should be simple and light on resource consumption.
Conventionally, many signal detection approaches that detect the presence of a desired signal or estimate its probability at the frame level have been proposed. One popular technique is to utilize a likelihood ratio (LR) test that is based on Gaussian, or normal distribution models. For example, a voice activity detector can be implemented utilizing an LR test. Such a voice activity detector typically employs a short-term spectral representation of the signal. In some implementations of this idea, a smoothed signal-to-noise ratio (SNR) estimate of respective frames can be used as an intermediate representation. Unfortunately, this technique, as well as other LR-based techniques, suffers from threshold selection and LR scores do not easily translate to true class probabilities. In order to convert from LR scores to true class probabilities, additional information such as prior probabilities of the hypotheses, for example, are required. Furthermore, such techniques typically assume that both the noise and the desired signal (e.g., speech) have normal distributions with zero mean, which can be an overly restrictive assumption. Conventional techniques that attempt to improve LR tests employ larger mixtures of models, which typically are computationally expensive.
Some detection systems render desired signal/no desired signal decisions at the frame level (e.g., they estimate a 0/1 indicator function) and smooth the decisions over time to arrive at a crude estimate of the probabilities. Some of these techniques utilize hard and/or soft voting mechanisms on top of the indicator functions estimated at the time-frequency atom level. A technique that is frequently utilized to estimate probabilities is a linear estimation model: ρ=A+BX; where ρ is the probability, X is the input (e.g., one or more LR scores or observed features like energies), and A and B are the parameters to be estimated. One such probability estimator, even though not explicitly formulated this way, adopts the linear model and utilizes the log of smoothed energy as the input. However, this linear model can render probabilities greater than 1 or less than 0 and a variance of error in estimation depends on the input (e.g., one or more variables).
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the invention. It is not intended to identify key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concepts of the invention in a simplified form as a prelude to the more detailed description that is presented later.
The present invention relates to systems and methods that provide signal (e.g., speech) detection using linear discriminative estimators based on a signal-to-noise ratio (SNR). An SNR is a measure of how noisy the signal is. As described above, in many signal detection cases simple detection (e.g., presence or absence) of a desired signal is not sufficient, and it is often necessary to estimate a probability of presence of the desired signal. The present invention overcomes the aforementioned deficiencies of conventional signal detection systems by providing systems and methods that employ linear discriminative estimators, wherein a logistic regression function(s) or a convolutional neural network(s), for example, is utilized to estimate a posterior probability that can be utilized to determine whether the desired signal is present in a frame (segment) and/or a respective frequency atom(s) (bin(s), unit(s)).
In general, the systems and methods utilize calibrated scores to define a desired level of performance and a logarithm of an estimated signal-to-noise ratio (e.g., nlpSNR and mel-nlpSMR) as input. In addition, the systems and methods incorporate spectral and temporal correlations and are configurable for either uni-level or multi-level architectures, which provides customization based on application needs and user desires. The multi-level architectures may use other signal representations (e.g., partial decisions and external features) as additional input. The foregoing novel systems and methods can be utilized in connection with applications such as speech detection, speech recognition, speech coding, noise adaptation, speech enhancement, microphone arrays and echo-cancellation, for example.
In one aspect of the present invention, a signal detection system that comprises an input component and a signal detection component is provided. The input component receives signals and transforms received signals into respective feature sets. The signal detection component determines whether a desired signal is present in a received signal, based at least in part on an associated feature set.
In another aspect of the present invention, a signal detection system comprising an input component, a model generator, a signal detection component, and an algorithm bank is provided. The input component can be utilized to receive signals that can include a desired signal and noise and determine a corresponding feature set based on a signal-to-noise ratio (SNR) (e.g., an estimated posterior SNR) associated with the desired signal. In order to compute this SNR, the model generator can be utilized to provide estimated noise via minima tracking and/or previous outputs from the signal detection component. Depending on a desired level of resolution, a normalized logarithm of the estimated posterior SNR (nlpSNR) or a mel-nlpSNR can be generated. This estimated SNR can be utilized to determine a probability indicative of whether the desired signal is present in the received signal. The probability can be determined via a logistic regression model, a convolutional neural network, as well as other classifiers, to render an estimated probability of a presence of the desired signal in the received signal. This probability can be utilized to render a decision, for example, by applying a thresholding technique. The foregoing can be utilized in connection with uni-level and bi-levels detection systems, which determine the aforementioned estimated probability at the frame level and atom/frame level, respectively.
In other aspects of the present invention, a signal enhancement system, methodologies that detect and enhance desired signals, and graphs illustrating exemplary results are provided. The enhancement system and methodology additionally include a logic unit that accepts the received signal and noise model, and generates an estimated clean desired signal. The graphs illustrate results from experiments executed at various SNRs for systems and methods employing the novel aspects described herein.
To the accomplishment of the foregoing and related ends, the invention comprises the features hereinafter fully described and particularly pointed out in the claims. The following description and the annexed drawings set forth in detail certain illustrative aspects and implementations of the invention. These are indicative, however, of but a few of the various ways in which the principles of the invention may be employed. Other objects, advantages and novel features of the invention will become apparent from the following detailed description of the invention when considered in conjunction with the drawings.
The present invention provides a simple and effective solution for signal detection and enhancement. The novel systems and methods estimate a probability of a presence of a desired signal in a received signal at a frame level and/or a bin (atom) level via at least one discriminative classifier (e.g., logistic regression-based and convolutional neural network-based classifiers) and based on an estimated posterior signal-to-noise ratio (SNR) (e.g., a normalized logarithm posterior SNR (nlpSNR) and a mel-nlpSNR). The novel systems and methods can be employed to facilitate speech detection, speech recognition, speech coding, noise adaptation, speech enhancement, microphone arrays and echo-cancellation, for example.
The present invention is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It may be evident, however, that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing the present invention.
The signal detection component 120 can utilize the feature set and/or received signal to facilitate determining whether the desired signal is present in the received signal. Such determination (as described in detail below) can be based on an estimate of a probability that the desired signal is present in the received signal. In one aspect of the invention, the estimated probability can be based on an estimated posterior signal-to-noise ratio (SNR) (e.g., a normalized logarithmic posterior SNR (nlpSNR) and a mel-nlpSNR), which can be determined with estimated noise from a noise model. In addition, the estimated probability can be determined via a classifier such as a linear discriminative classifier (e.g., logistic regression and convolutional neural network). It is to be appreciated that the system 100 can be utilized to facilitate speech detection, speech recognition, speech coding, noise adaptation, speech enhancement, microphone arrays and echo-cancellation, for example.
An estimated noise utilized to generate the estimated SNR can be provided to the input component 210 by the model generator 220, for example, via a noise model. In one instance, the model generator 220 can utilize minima tracking and/or Bayesian adaptive techniques to estimate the noise model. For example, a two-level online automatic noise tracker can be employed, wherein an initial noise estimate can be bootstrapped via a minima tracker and a maximum a posteriori estimate of the noise (power) spectrum can be obtained. It is to be appreciated that various other noise tracking algorithms can be employed in accordance with aspects of the present invention. In addition, the model generator 220 can utilize one or more outputs from the signal detection component 230 to facilitate estimating the noise model.
The input component 210 can utilize the estimated noise to determine the estimated posterior SNR (ξ(k,t)) via Equation 1.
wherein ξ(k,t) is a ratio of energy in a given frame Y to an estimated noise energy {circumflex over (λ)}, k is a frequency index in the frame Y, and t is a time index into the frame Y. An estimated actual SNR (e.g., prior SNR) can additionally or alternatively be utilized as a feature set. A power spectrum can be computed via a transform such as a Fast Fourier Transforms (FFTs), a windowed FFT, and/or a modulated complex lapped transform (MCLT), for example. As known, the MCLT can be a particular form of a cosine-modulated filter-bank that allows for virtually perfect reconstruction.
Since a feature set can be provided to a learning machine, preprocessing can be employed to improve generalization and learning accuracy. For example, since short-term spectra of a desired signal such as speech, for example, can be modeled by a log-normal distribution, a logarithm of the SNR estimate, rather than the SNR estimate ξ(k,t), can be utilized. In addition, the input can be variance normalized, for example, to one. Furthermore, respective variance coefficients can be pre-computed, for example, over a training set(s) and utilized as a normalizing factor. Thus, the feature set can be a normalized logarithm of the estimated posterior SNR (nlpSNR) as defined in Equation 2.
where y(k,t) is the feature in a frequency-time bin (k,t) and σ(k) is a variance-normalizing factor.
Upon generating the feature set, it can be conveyed to the signal detection component 230, which can utilize the feature set to provide an indication as to whether the desired signal is present in the received signal. Such indication can be based on an estimated probability of a presence of the desired signal in the received signal, wherein the estimated probability can be generated via logistic regression, a convolutional neural network, as well as other classifiers. Such classifiers can be stored in the algorithm bank 240 and selectively (e.g., manually or automatically) chosen and utilized, based at least in part on the application.
In one aspect of the invention, an estimated probability ρx can be determined by Equation 3.
where X is an input and A and B are system parameters that can be estimated by minimizing a cross-entropy error function, for example, via Equation 4.
where tx represents target labels for a training data X and, hence, is discriminative. Additionally, this function can provide a maximum likelihood estimate of a class probability.
Equation 3 can provide a very good estimate of a posterior probability of a membership of a class ρ(C|X) for a wide variety of class conditional densities of data X. If such densities are multivariate Gaussians with equal variances, then this estimate can provide a substantially exact posterior probability; however, it is to be understood that this is not a necessary condition. Furthermore, this function can provide additional advantages over Gaussian models; for example, setting thresholds can be easier, and if the input vector X includes data from adjacent time and frequency atoms, this function can provide an efficient mechanism that can incorporate both temporal and spectral correlation into the decision without requiring an accurate match of underlying distributions. The parameters can be easily determined utilizing gradient descent-based learning algorithms. Thus, this technique can provide both richness and simplicity.
After determining the probability of the presence of the desired signal in the received signal, the probability can be utilized to render a decision. For example, the probability can be utilized to provide a Boolean (e.g., “true” and “false”) or logic (e.g., 0 and 1) value or any desired indicia that can indicate a decision. This decision can be utilized to determine whether further processing should be performed on the received signal. For example, if it is determined that the desired signal is present, techniques can be employed to enhance or extract the desired signal or suppress an undesired signal. Moreover, the probability can be conveyed to the model generator 220 to facilitate creating and/or updating noise models.
For example, a scale can be defined wherein a probability of 1 indicates a one hundred percent confidence (a certain event) that the desired signal is present; a probability of 0 can indicate a one hundred percent confidence that the desired signal is absent; a probability of 0.5 can indicate a fifty percent confidence that the desired signal is present, etc. When rendering true positives (determining the desired signal is present when in fact it is present) is more important than mitigating false positives (determining the desired signal is present when it is not present), the threshold can be set closer to 0 such that a slight probability that the desired signal is presents results in a decision that indicates the desired signal is present. However, when it is more important to mitigate false positives than render true positives, the threshold can be defined closer to 1 such that a slight probability that the desired signal is present results in a decision that indicates the desired signal is absent.
Likewise, when rendering true negatives (determining the desired signal is absent when in fact it is absent) is more important than mitigating false negatives (determining the desired signal is absent when it is present), the threshold can be set closer to 1 such that a slight probability that the desired signal is present results in a decision that indicates the desired signal is absent, and when it is more important to mitigate false negatives than render true negatives, the threshold criteria can be defined closer to 0 such that a slight probability that the desired signal is present results in a decision that indicates the desired signal is present. It is to be appreciated that the above examples are provided for explanatory purposes and do not limit the invention. Essentially, the threshold can be variously defined and customized based on the application and user needs.
The feature generator 410 receives one or more atoms from one or more frames and transforms the atoms into a feature set(s). Referring briefly to
Returning to
y(k,t)=[y(k−a:k+a,t−i:t+i)], Equation 5:
which is the concatenation of the feature y in a frequency-time bin (k,t). To mitigate any time delay in processing, if desired, the feature can be strictly causal and need not include future frames.
The nlpSNR vectors can be utilized by respective detectors 420 to generate estimated posterior probabilities P(k,t) that correspond to whether the desired signal is present in the received signal at the atom level. These atom level estimated posterior probabilities can be concatenated (e.g., via a similar technique utilized to concatenate y) and the concatenation can be conveyed to the frame detector 430. The frame detector 430 can utilize the concatenation to determine a frame level estimated posterior probability indicative of whether the desired signal is present in the received signal.
By way of example, atoms (e.g., atom 530) from a frame (e.g., frame 560) can be conveyed to the feature generator 620. As noted above, a frame can comprise a plurality of atoms for a short time interval around the time instant t that can be indexed as frequency/time pairs (e.g., (fi,t) can be utilized to index atom 530). The atoms can be utilized to generate vectors representing mel-normalized logarithms of estimated posterior SNRs (mel-nlpSNRs). In general, mel-nlpSNR's are lower in resolution than nlpSNRs. For example, both |Y|2 and {circumflex over (λ)} (as described above) can be converted into mel-band energies M|Y|2 and M{circumflex over (λ)} before an nlpSNR is generated. The corresponding feature set can be defined by Equation 6.
where yM(k,t) is the mel-based feature in a frequency-time bin (k,t) and σM(k) is a mel-based variance-normalizing factor.
The mel-nlpSNR vectors can be conveyed to the frame detector 610 in a form defined by Equation 7.
yM(k,t)=[y(k−a:k+a,t−i:t+i)], Equation 7:
where yM is the mel-band derivative of y (described above). Input in the form of Equation 7 can be utilized by the frame level detector 610 to generate an estimated frame level probability via logistic regression, convolution, etc.
Upon determining a frame level and/or atom level estimated posterior SNR (e.g., nlpSNR and mel-nlpSNR), the analyzer 720 conveys the SNR to a discriminator 740. The discriminator 740 can employ various techniques to estimate a posterior probability that speech is present in the received signal. As described in detail above, the discriminator 740 can employ a logistic regression or convolutional neural network approach, for example. In addition, the estimation can be performed at the frame level and/or the atom level, wherein the results at the atom level can be utilized to generate a more refined frame level estimation.
The output of the discriminator 740 is an estimated posterior probability that speech is present in the received signal. This output can be utilized to render a decision based on criteria that defines a decision-making probability level. In addition, the discriminator 740 can convey the output to the model generator 730, wherein the estimated posterior probability is utilized to facilitate creating a noise model or refining an existing noise model. The model generator 730 can provide the new or updated noise model to the analyzer 720 to be utilized as described previously. Thus, the analyzer 720 can utilize the new or updated noise model to facilitate determining SNRs. The new or updated SNR generally improves SNR determination. In addition, the model generator 730 can provide the noise model to various other components for further analysis, processing and/or decision-making.
Upon determining an estimated posterior SNR (e.g., nlpSNR and mel-nlpSNR), the analyzer 820 can convey the SNR to the discriminator 850, which can estimate a posterior probability that speech is present in the received signal. Various techniques can be employed to estimate the posterior probability such as linear discriminators (e.g., logistic regression and convolutional neural network). The estimated posterior probability can be conveyed to the model generator 840, wherein it can be utilized to facilitate creating a noise model or refining an existing noise model. The model generator 840 can provide the new or updated noise model to the analyzer 820 and to the logic unit 830.
The analyzer 820 can utilize the new or updated noise model to facilitate determining SNRs. The logic unit 830 can utilize the noise model and the received signal, which can be provided by the receiver 810, to provide an estimate of a clean speech signal. For example, the logic unit 830 can compute a gain, and utilize the gain to enhance the speech. The enhanced speech can be output.
At reference numeral 930, the estimated posterior SNR can be preprocessed. In one aspect of the invention, since short-term spectra can be modeled by a log-normal distribution, a logarithm of the estimate posterior SNR can be utilized rather than the estimated posterior SNR. In addition, an associated variance can be normalized to one. Furthermore, variance coefficients can be pre-computed, for example, over a training set(s) and utilized as a normalizing factor. The foregoing provides for a feature set that can be referred to as a normalized logarithm of the estimated posterior SNR (nlpSNR). This feature set is generally employed when atom level detection is desired to improve resolution. The results from the atom level detection can then be employed for frame level detection. When atom level detection is not desired, a mel-normalized logarithm of the estimated posterior SNR (mel-nlpSNR) can be determined and employed to generate a frame level estimated posterior SNR.
At reference numeral 940, the SNR is utilized to determine a probability that the desired signal is in the received signal. It is to be appreciated that a logistic regression model, a convolutional neural network, as well as other classifiers, can be employed to facilitate estimating this probability. The estimated probability can be utilized to render a decision as to whether the desired signal is present. For example, thresholding can be utilized to render a decision such as “true” or “false,” 1 or 0, “yes” or “no,” and the like, which indicate whether or not the desired signal is present. It is to be appreciated that the methodology 900 can be employed to facilitate speech detection, speech recognition, speech coding, noise adaptation, speech enhancement, microphone arrays and echo-cancellation, for example.
In order to provide a context for the various aspects of the invention,
Moreover, those skilled in the art will appreciate that the inventive methods may be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, mini-computing devices, mainframe computers, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like. The illustrated aspects of the invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. However, some, if not all aspects of the invention can be practiced on stand-alone computers. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
With reference to
The system bus 1118 can be any of several types of bus structure(s) including the memory bus or memory controller, a peripheral bus or external bus, and/or a local bus using any variety of available bus architectures including, but not limited to, Industrial Standard Architecture (ISA), Micro-Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), Universal Serial Bus (USB), Advanced Graphics Port (AGP), Personal Computer Memory Card International Association bus (PCMCIA), and Small Computer Systems Interface (SCSI).
The system memory 1116 includes volatile memory 1120 and nonvolatile memory 1122. The basic input/output system (BIOS), containing the basic routines to transfer information between elements within the computer 1112, such as during start-up, is stored in nonvolatile memory 1122. By way of illustration, and not limitation, nonvolatile memory 1122 can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory 1120 includes random access memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).
Computer 1112 also includes removable/non-removable, volatile/non-volatile computer storage media.
It is to be appreciated that
A user enters commands or information into the computer 1112 through input device(s) 1136. Input devices 1136 include, but are not limited to, a pointing device such as a mouse, trackball, stylus, touch pad, keyboard, microphone, joystick, game pad, satellite dish, scanner, TV tuner card, digital camera, digital video camera, web camera, and the like. These and other input devices connect to the processing unit 1114 through the system bus 1118 via interface port(s) 1138. Interface port(s) 1138 include, for example, a serial port, a parallel port, a game port, and a universal serial bus (USB). Output device(s) 1140 use some of the same type of ports as input device(s) 1136. Thus, for example, a USB port may be used to provide input to computer 1112, and to output information from computer 1112 to an output device 1140. Output adapter 1142 is provided to illustrate that there are some output devices 1140 like monitors, speakers, and printers, among other output devices 1140, which require special adapters. The output adapters 1142 include, by way of illustration and not limitation, video and sound cards that provide a means of connection between the output device 1140 and the system bus 1118. It should be noted that other devices and/or systems of devices provide both input and output capabilities such as remote computer(s) 1144.
Computer 1112 can operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1144. The remote computer(s) 1144 can be a personal computer, a server, a router, a network PC, a workstation, a microprocessor based appliance, a peer device or other common network node and the like, and typically includes many or all of the elements described relative to computer 1112. For purposes of brevity, only a memory storage device 1146 is illustrated with remote computer(s) 1144. Remote computer(s) 1144 is logically connected to computer 1112 through a network interface 1148 and then physically connected via communication connection 1150. Network interface 1148 encompasses communication networks such as local-area networks (LAN) and wide-area networks (WAN). LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ring and the like. WAN technologies include, but are not limited to, point-to-point links, circuit switching networks like Integrated Services Digital Networks (ISDN) and variations thereon, packet switching networks, and Digital Subscriber Lines (DSL).
Communication connection(s) 1150 refers to the hardware/software employed to connect the network interface 1148 to the bus 1118. While communication connection 1150 is shown inside computer 1112, it can also be external to computer 1112. The hardware/software necessary for connection to the network interface 1148 includes, for exemplary purposes only, internal and external technologies such as, modems including regular telephone grade modems, cable modems and DSL modems, ISDN adapters, and Ethernet cards.
One possible communication between a client 1210 and a server 1220 can be in the form of a data packet adapted to be transmitted between two or more computer processes. The system 1200 includes a communication framework 1240 that can be employed to facilitate communications between the client(s) 1210 and the server(s) 1220. The client(s) 1210 are operably connected to one or more client data store(s) 1250 that can be employed to store information local to the client(s) 1210. Similarly, the server(s) 1220 are operably connected to one or more server data store(s) 1230 that can be employed to store information local to the servers 1240.
As used in this application, the term “component” is intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a computer component. In addition, one or more components can reside within a process and/or thread of execution, and a component can be localized on one computer and/or distributed between two or more computers. Furthermore, a component can be an entity (e.g., within a process) that an operating system kernel schedules for execution. Moreover, a component can be associated with a context (e.g., the contents within system registers), which can be volatile and/or non-volatile data associated with the execution of the thread.
What has been described above includes examples of the present invention. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the present invention, but one of ordinary skill in the art may recognize that many further combinations and permutations of the present invention are possible. Accordingly, the present invention is intended to embrace all such alterations, modifications, and variations that fall within the spirit and scope of the appended claims.
In particular and in regard to the various functions performed by the above described components, devices, circuits, systems and the like, the terms (including a reference to a “means”) used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., a functional equivalent), even though not structurally equivalent to the disclosed structure, which performs the function in the herein illustrated exemplary aspects of the invention. In this regard, it will also be recognized that the invention includes a system as well as a computer-readable medium having computer-executable instructions for performing the acts and/or events of the various methods of the invention.
In addition, while a particular feature of the invention may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms “includes,” and “including” and variants thereof are used in either the detailed description or the claims, these terms are intended to be inclusive in a manner similar to the term “comprising.”
This application claims the benefit of U.S. Provisional Patent Application Ser. No. 60/513,659 filed on Oct. 23, 2003 and entitled “LINEAR DISCRIMINATIVE SPEECH DETECTORS USING POSTERIOR SNR,” the entirety of which is incorporated herein by reference.
| Number | Name | Date | Kind |
|---|---|---|---|
| 20030236661 | Burges et al. | Dec 2003 | A1 |
| Number | Date | Country | |
|---|---|---|---|
| 20050091050 A1 | Apr 2005 | US |
| Number | Date | Country | |
|---|---|---|---|
| 60513659 | Oct 2003 | US |
