Source: https://patents.google.com/patent/US6760701?oq=flatulence
Timestamp: 2018-03-23 04:02:21
Document Index: 336489255

Matched Legal Cases: ['application No. 60', 'art 2', 'Application No. 60', 'application No. 60', 'application No. 60', 'application No. 60']

US6760701B2 - Subword-based speaker verification using multiple-classifier fusion, with channel, fusion, model and threshold adaptation - Google Patents
Subword-based speaker verification using multiple-classifier fusion, with channel, fusion, model and threshold adaptation Download PDF
US6760701B2
US6760701B2 US10042832 US4283202A US6760701B2 US 6760701 B2 US6760701 B2 US 6760701B2 US 10042832 US10042832 US 10042832 US 4283202 A US4283202 A US 4283202A US 6760701 B2 US6760701 B2 US 6760701B2
US10042832
US20030009333A1 (en )
One type of voice recognition system is a text-dependent automatic speaker verification system. The text-dependent ASV system requires that the user speak a specific password or phrase (the “password”). This password is determined by the system or by the user during enrollment. However, in most text-dependent ASV systems, the password is constrained to be within a fixed vocabulary, such as a limited number of numerical digits. The limited number of password phrases gives an imposter a higher probability of discovering a person's password, reducing the reliability of the system.
Modeling of speech has been done at the phrase, word, and subword level. In recent years, several subword-based speaker verification systems have been proposed using either Hidden Markov Models (“HMM”) or Artificial Neural Network (“ANN”) references. Modeling at the subword level expands the versatility of the system. Moreover, it is also conjectured that the variations in speaking styles among different speakers can be better captured by modeling at the subword level.
All ASV systems include at least two components, an enrollment component and a testing component. The enrollment component is used to store information concerning a user's voice. This information is then compared to the voice undergoing verification (testing) by the test component. The system of the present invention includes inventive enrollment and testing components, as well as a third, “bootstrap” component. The bootstrap component is used to generate data which assists the enrollment component to model the user's voice.
In the event that only a small amount of data is available for modeling a speaker, the resulting classifier is very likely to be biased. Data resampling artificially expands the size of the sample pool and therefore improves the generalizations of the classifiers. One of the embodiments of the classifier fusion and data resampling scheme is a “leave-one-out” data resampling method.
The test component is the component which performs the verification. During testing or verification, the system first accepts “test speech” and index information from a user claiming to be the person identified by the index information. Voice data indexed in the database is retrieved and used to process the test speech sample.
During verification, the user speaks the password into the system. This “test speech” password undergoes preprocessing, as previously described, with respect to the enrollment component. The next step is to perform channel adaptation.
After channel adaption, feature extraction is performed on the test sample. This occurs as previously described with respect to the enrollment component. After feature extraction, it is desired to locate, or “spot” the phrases in the test speech and simultaneously avoid areas of background noise.
After key word/key phrase spotting, automatic speech segmentation occurs. Preferably the automatic speech segmentation is not “blind” segmentation (although “blind” segmentation could be used), but is “force” alignment segmentation. This force segmentation uses the segments previously obtained by the blind segmentation performed in the enrollment component. The test speech is therefore segmented using the segmentation information previously stored. The “force” segmentation results in the identification of subword borders. The subwords undergo multiple classifier fusion.
The multiple classifiers of the enrollment component are used to “score” the subword data, and the scores are the fused, or combined. The result of the fusion is a “final score.” The final score is compared to the stored threshold. If the final score exceeds or equals the threshold, the test sample is verified as the user's. If the final score is less than the threshold, the test sample is declared not to be the user's. The final score and date of verification, as well as other related details, may be stored in the database as well.
3. “Bootstrapping” Component Summary
Bootstrapping is used to generate a pool of speech data representative of the speech of nonspeakers, or “antispeakers.” This data is used during enrollment to train the discriminant training-based classifiers. Bootstrapping involves obtaining voice samples from antispeakers, preprocessing the voice samples (as in the enrollment phase), and inverse channel filtering the preprocessed voice samples. Inverse channel filtering removes the characteristics of the channel on which the antispeaker voice sample is obtained. After inverse channel filtering, feature generation and automatic blind voice segmentation occur, as in the enrollment component. The segments and feature vectors are stored in an antispeaker database for use by the enrollment component.
1. Enrollment Component—Detailed Description
The enrollment component is used to store information (using supervised learning) about a known user's voice into a voice print database, so that this information is available for future comparisons. In the preferred embodiment, the enrollment component also stores information concerning the channel on which the user provides the speech, the “enrollment channel” into the voice print database.
Ŝ(ω)=S(ω)×(ω)
While CMS may be used alone to remove the effects of the channel distortion, the cepstral mean may include information other than the estimate of the time-invariant convolutional distortion, such as coarse spectral distribution of the speech itself. Pole filtering attempts to decouple the speech information from the channel information in the cepstral mean. Since cepstrum is the weighted combination of LP poles or spectral components, the effect of individual components on the cepstral mean was examined. It was found that broad band-width components exhibited smoother frequency characteristics corresponding to the “roll-off” of channel distortion, assuming that narrow band-width components in the inverse filter were influenced more by speech characteristics. Thus, the narrow band-width LP poles were selectively deflated by broadening their bandwidth and keeping their frequency the same.
After preprocessing 30, feature extraction 50 is performed on the processed speech. Feature extraction may occur after (as shown) or simultaneously with the step of channel estimation 40 (in parallel computing embodiments). Spectral features are represented by speech feature vectors determined within each frame of the processed speech signal. In feature extraction 50, spectral feature vectors can be obtained with conventional methods such as linear predictive (LP) analysis to determine LP cepstral coefficients, Fourier Transform Analysis and filter bank analysis. One method of feature extraction 50 is disclosed in U.S. Pat. No. 5,522,012, entitled “Speaker Identification and Verification System,” issued on May 28, 1996 and incorporated herein by reference. A preferred method for obtaining spectral feature vectors is performing a 12th order TP-PFC obtained from a 12th order linear prediction (LP) with alpha=0.7.
The preferred technique for subword generation 70 is automatic blind speech segmentation, or “Blind Clustering” such as disclosed in U.S. patent application Ser. No. 08/827,562 entitled “Blind Clustering of Data With Application to Speech Processing Systems”, filed on Apr. 1, 1997, and its corresponding U.S. provisional application No. 60/014,537 entitled “Blind Speech Segmentation”, filed on Apr. 2, 1996, both of which are herein incorporated by reference. During enrollment in the speaker verification system, the automatic blind speech segmentation determines the number of subwords in the password and the location of optimal subword boundaries. Additionally, the subword durations are normalized by the total duration of the voice phrase and stored in the voice print database 115 for subsequent use during testing (force segmentation).
The subword data from the speaker being trained is labeled as enrollment speaker data. Because there is a no linguistic labelling information in the antispeaker database 110, the entire database 110 is searched for the closet subword data from other speakers. This data is labeled the anti-speaker data. The mean vector and covariance matrix of the subword segments obtained from subword generation are used to find the “close” subwords. An anti-speaker module 120 searches the antispeaker database 110 to find the “close” subwords of antispeaker data, which are used in the NTN model 20 . Preferably, 20 “close” subwords are identified. The anti-speaker data in the antispeaker database 110 is either manually created, or created using a “bootstrapping” component, described below with reference to FIG. 11.
Because a “leave-one-out” system 100 is employed with multiple (N) samples, the classifier models 80, 90 are trained by comparing antispeaker data with N-1 samples of enrollment speech. Both modules 80, 90 can determine a score for each spectral feature vector of a subword segment. The individual scores of the NTN 80 and GMM 90 modules can be combined, or “fused” by a classifier fusion module 130 to obtain a composite score for the subword. Since these two modeling approaches tend to have errors that are uncorrelated, it has been found that performance improvements can be obtained by fusing the model outputs 130. In the preferred embodiment, the results of the neural tree network 80 and the Gaussian mixture model 90 are fused 130 using a linear opinion pool, as described below. However, other ways of combining the data can be used with the present invention including a log opinion pool or a “voting” mechanism, wherein hard decisions from both the NTN and GMM are considered in the voting process.
With continued reference to FIG. 1A, one of modules used to model the subword segments of the user password is an NTN module 80. The NTN is a hierarchical classifier that uses a tree architecture to implement a sequential linear decision strategy. Specifically, the training data for a NTN consists of data from a target speaker, labeled as one, along with data from other speakers (antispeaker data) that are labeled as zero. The NTN learns to distinguish regions of feature space that belong to the target speaker from those that are more likely to belong to an impostor. These regions of feature space correspond to leaves in the NTN that contain probabilities. These probabilities represent the likelihood of the target speaker having generated data that falls within that region of feature space. In the preferred embodiment, NTN modeling 220 is performed using the following forward pruning criteria: (a) maximum depth of four, (b) pruned nodes containing less than 10% of data vectors at the root. The NTN scores for individual feature vectors are accumulated across subwords by an NTN scoring algorithm 145. The functioning of NTN networks with respect to speaker recognition is disclosed in K. R. Farrell, R. J. Mammone, and K. T. Assaleh, “Speaker Recognition using Neural Networks and Conventional Classifiers”, IEEE Trans. Speech and Audio Processing, 2(1), part 2 (1994), and U.S. patent application 08/159,397, filed Nov. 29, 1993, entitled “Rapidly Trainable Neural Tree Network”, U.S. patent application Ser. No. 08/479,012 entitled “Speaker Verification System,” U.S. patent application Ser. No. 08/827,562 entitled “Blind Clustering of Data With Application to Speech Processing Systems”, filed on Apr. 1, 1997, and its corresponding U.S. Provisional Application No. 60/014,537 entitled “Blind Speech Segmentation”, filed on Apr. 2, 1996, each of which is incorporated herein by reference in its entirety.
As discussed previously, a Gaussian mixture model (GMM) 90 is also used to model each of the subwords. In the GMM, a region of feature space for a target speaker is represented by a set of multivariate Gaussian distributions. In the preferred embodiment, the mean vector and covariance matrix of the subword segments are obtained as a by-product of subword generation using automatic blind speech segmentation and are saved as part of the GMM module, as described in U.S. patent application Ser. No. 08/827,562 entitled “Blind Clustering of Data With Application to Speech Processing Systems”, filed on Apr. 1, 1997, and its corresponding U.S. provisional application No. 60/014,537 entitled “Blind Speech Segmentation”, filed on Apr. 2, 1996, both of which are herein incorporated by reference. The GMM probability distribution function is expressed as: p  ( x ) = ∑ i = 1 G   P  ( ω i )  p  ( x / μ i , σ 2 ) .
Each of the G mixture components is defined by a mixture weight P(ωi) and multi-dimensional normal distribution function p(x/μi, σι 2), where μi is the mean vector and σιis the covarience matrix. In the preferred embodiment, the normal distribution is constrained to have a diagonal covariance matrix defined by the vector σι. The PDF is used to produce the sub-word GMM score.
In the preferred embodiment, a classifier fusion module 130 using the linear opinion pool method combines the NTN score and the GMM score. Use of the linear opinion pool is referred to as a data fusion function, because the data from each classifier is “fused,” or combined.
The data fusion function for n classifiers, S(α), is governed by the following linear opinion pool equation: S  ( α ) = ∑ i = 1 n   α i  s i
In this equation S(α) is the probability of the combined system, αi are weights, and si(α) is the probability output by the ith classifier, and n is the number of classifiers; αi is between zero and one and the sum of all αi's is equal to one. If two classifiers are used (n=2), s1 is the score of the first classifier and s2 is the score of the second classifier. In this instance the equation becomes:
S=αs 1+(1−α)s 2
The variable α is set as a constant (although it may be dynamically adapted as discussed below), and functions to provide more influence on one classifier method as opposed to the other. For example, if the NTN method 80 was found to be more accurate, the first classifier s1 would be more important, and α would be made greater than 0.5, or its previous value. Preferably, α is only incremented or decremented by a small amount, ε.
Once the variables in the fusion equation are known, a threshold value 140 is output and stored in the voice print database 115. The threshold value output 140 is compared to a “final score” in the testing component to determine whether a test user's voice has so closely matched the model that it can be said that the two voices are from the same person.
2. Testing Component—Detailed Description
The total distance function D is of the form: D = ∑ n = 1 N   d  ( R  ( n ) , T  ( w  ( n ) ) ) .
The expression d(R(n), T(w(n))) is the local distance between the frame n of the reference pattern and the frame m=w(n) of the test pattern.
The reference utterance 350 could be chosen in a number of ways. The preferred method is to select the enrollment utterance with the minimum duration. selection = arg   min i  { L i } where   L i   is   the   utterance   length   of   utterances   i = 1 , 2 , 3 , 4.
A second approach to select the enrollment utterance with median duration. Thus the enrollment utterances of lengths L1, L2, L3, L4 are sorted in order of increasing length, for example [L2, L4, L1, L3] (using the utterances of FIG. 6). In this case, L4 is the reference utterance of because it is the median value.
selection = arg   min i   L avg - L i    i = 1 , 2 , 3 , 4
A fourth approach is to select an utterance with minimum combined distortion with respect to the other utterances. A distortion matrix D is created: D _ = d 11 d 12 d 13 d 14 d 21 d 22 d 23 d 24 d 31 d 32 d 33 d 34 d 41 d 42 d 43 d 44
The distortion, dij is the DTW distortion between utterance i and utterance j. The utterance-wise combined distortion is: d i = ∑ j = 1 4   d ij + ∑ j = 1 4   d ji   i = 1 , 2 , 3 , 4
The utterance selected is the one with minimum di. Selection = arg   min i   d i
After selecting 300 a reference utterance 350, all the remaining utterances are “warped” 310 onto the reference utterance 350 using the DTW algorithm. FIG. 6 shows four utterances 370, 380, 390 and 400 of different lengths (L1, L2, L3, L4) “warped” onto the reference utterance 350, which produces four “warped” utterances 410, 420, 430 and 440 of length Lref. The four warped utterances 410, 420, 430 and 440 are averaged 320 to form a reference template 450 of length Lref.
As previously described with respect to FIG. 1A, during the enrollment component 10 GMM modeling 90 was performed. The GMM modeling 90 is used in the test component subword generation 210 to “force align” the test phrase into segments corresponding to the previously formed subwords. Using the subword GMMs as reference models, Viterbi or Dynamic programming (DP) based algorithms are used to locate the optimal boundaries for the subword segments. Additionally, the normalized subword duration (stored during enrollment) is used as a constraint for force alignment since it provides stability to the algorithm. Speech segmentation using force alignment is disclosed in U.S. patent application Ser. No. 08/827,562 entitled “Blind Clustering of Data With Application to Speech Processing Systems”, filed on Apr. 1, 1997, and its corresponding U.S. provisional application No. 60/014,537 entitled “Blind Speech Segmentation”, filed on Apr. 2, 1996, both of which are herein incorporated by reference in their entirety.
After subword generation 210 is performed, scoring 240, 250 using the techniques previously described with respect to FIG. 2 (i.e. multiple classifiers such as GMM 230 and NTN 220) is performed on the subwords. Scoring using the NTN and GM classifiers 220, 230 is disclosed in U.S. patent application Ser. No. 60/064,069, entitled “Model Adaption System And Method For Speaker Verification,” filed on Nov. 3, 1997 by Kevin Farrell and William Mistretta, U.S. patent application Ser. No. 08/827,562 entitled “Blind Clustering of Data With Application to Speech Processing Systems”, filed on Apr. 1, 1997, and its corresponding U.S. provisional application No. 60/014,537 entitled “Blind Speech Segmentation”, filed on Apr. 2, 1996, each of which is herein incorporated by reference in its entirety.
With continued reference to FIG. 2, the classifier fusion module 260 outputs a “final score” 270. The “final score” 270 is then compared 280 to the threshold value 140. If the “final score” 270 is equal to or greater than the threshold value 140 obtained during enrollment, the user is verified. If the “final score” 270 is less than the threshold value 140 then the user is not verified or permitted to complete the transaction requiring verification.
As shown in FIG. 2, a fusion adaptation module 290 is connected to the classifier fusion module 280. The fusion adaptation module 290 changes the constant, α, in the linear pool data fusion function described previously with respect to FIG. 2, which is: S  ( α ) = ∑ i = 1 n   α i  s i
In the present invention two classifiers are used (NTN 80, 220 and GMM 90, 230) and s1 is the score of the first classifier and s2 is the score of the second classifier. In this instance the equation becomes:
S=αs 1+(1−α)s2
The fusion adaptation module 290 dynamically changes α to weigh either the NTN (s1) or GMM (s2) classifiers more than the other, depending on which classifier turns out to be more indicative of a true verification.
For example, the previous four test speech samples in which successful verification occurred may be recalled from the voice print database 115, as well as the four initial training samples of enrollment speech. This doubles the number of training samples from four to eight. In order to limit the number of training samples, a “forget” factor may be built into the system, the forget feature may discard one or more samples. For example, only the latest eight samples may be remembered, or only the initial four enrollment speech samples and the newest four successful test samples. The number of samples, and which samples are used, may depend on the tolerance for false-positive results and false-negative results, since the model adaptation will change these probabilities.
Model adaptation 540 may also occur as described in copending Provisional Application Serial No. 60/064,069, entitled “Model Adaption System And Method For Speaker Verification,” filed on Nov. 3, 1997 by Kevin Farrell and William Mistretta.
3. “Bootstrapping” Component
Because the enrollment component 10 uses the “closest” antispeaker data to generate the threshold value 140, the antispeaker database 110 must be initially be filled with antispeaker data. The initial antispeaker data may be generated via artifical simulation techniques, or can be obtained from a pre-existing database, or the database may be “bootstrapped” with data by the bootstrapping component.
1. An automatic speaker verification system comprising:
a receiver, the receiver obtaining enrollment speech over an enrollment channel;
a means, connected to the receiver, for developing an estimate of the enrollment channel;
a first storage device, connected to the receiver, for storing the enrollment channel estimate;
a means for extracting predetermined features of the enrollment speech;
a means, operably connected to the extracting means, for segmenting the predetermined features of the enrollment speech, wherein the features are segmented into a plurality of subwords using automatic blind speech segmentation; and
at least one classifier, connected to the segmenting means, wherein the classifier models the plurality of subwords and outputs one or more classifier score.
2. The automatic speaker verification system of claim 1, further comprising:
an analog to digital converter, connected to the receiver, for providing the obtained enrollment speech in a digital format.
at least one Gaussian mixture model classifier, the Gaussian mixture model classifer resulting in a first classifier score; and
at least one neural tree network classifier, the neural tree network classifer resulting in a second classifier score.
a means, connected to the receiver, for developing an estimate of the enrollment channel wherein said estimating means comprises a means for creating a filter representing characteristics of the enrollment channel, by dissecting the speech into its individual frequency components, selecting those components whose bandwidths are larger than a preset threshold to be those contributed by the channel, and then recombining those components that are contributed by the channel to create a channel estimate;
a means, operably connected to the extracting means, for segmenting the predetermined features of the enrollment speech, wherein the features are segmented into a plurality of subwords; and
at least one classifier, connected to the segmenting means, wherein the classifier models the plurality of subwords and outputs one or more classifier scores.
9. An automatic speaker verification method, comprising:
obtaining enrollment speech over an enrollment channel;
storing an estimate of the enrollment channel;
extracting predetermined features of the enrollment speech;
segmenting the enrollment speech, wherein the enrollment speech is segmented into a plurality of subwords using automatic blind speech segmentation; and
modeling the plurality of subwords using one or more classifier models resulting in an output of one of more classifier scores.
10. The automatic speaker verification method of claim 9, further comprising the steps of:
digitizing the obtained enrollment speech; and
preprocessing the digitized enrollment speech.
11. The automatic speaker verification method of claim 9, wherein the step of modeling comprises the step of scoring at least one neural tree network classifier.
scoring at least one Gaussian mixture model classifier, the Gaussian mixture model classifer resulting in a first classifier score;
scoring at least one neural tree network classifier, the Gaussian mixture model classifer resulting in a second classifier score;
fusing the first and second classifier scores.
13. The automatic speaker verification method of claim 9, further comprising the steps of:
weighing the scores from the classifier models with a fusion constant; and combining the weighted scores resulting in a final score for the combined system.
14. The automatic speaker verification method of claim 9, wherein the step of storing an estimate of the enrollment channel comprises the step of creating a filter representing characteristics of the enrollment channel.
creating an estimate of the enrollment channel wherein the estimate of the enrollment channel comprises the steps of dissecting the speech into its individual frequency components, selecting those individual frequency components whose bandwidths are larger than a preset threshold to be those components that are contributed by the channel, and then recombining those components that are contributed by the channel to create the enrollment channel estimate;
inverse filtering the enrollment speech to create inverse filtered enrollment speech;
receiving test speech over a testing channel;
inverse filtering the test speech to create inverse filtered test speech; and
determining whether the inverse filtered test speech comes from the same person as the inverse filtered enrollment speech.
16. The automatic speaker verification method of claim 15, wherein the step of inverse filtering the enrollment speech comprises the step of creating a filter representing inverse characteristics of the enrollment channel.
obtaining two or more samples of enrollment speech;
processing each sample of enrollment speech to form corresponding utterances;
obtaining test speech;
identifying one or more key words/key phrases in the test speech, including the steps of:
selecting a reference utterance from one of the utterances;
warping the remaining samples of the enrollment speech to the reference utterance;
averaging one or more of the warped utterances to generate a reference template;
calculating a dynamic time warp distortion for the reference template and test speech; and
choosing a portion of the test utterance which has the least dynamic time warp distortion; and
comparing the identified key word/key phrases to the enrollment speech to determine whether the test speech and enrollment speech are from the same person.
19. The automatic speaker verification method of claim 18, wherein the step of selecting a reference utterance comprises the step of: choosing the utterance with minimum duration.
obtaining test speech from a user over a test channel;
processing the test speech to remove the effects of the test channel; and
comparing the processed test speech with speech data from a known user, including the steps of:
extracting features of the test speech;
generating subwords based on the extracted features;
scoring the subwords using one or more model classifiers;
fusing the results of the model classifiers to obtain a final score; and
verifying the user if the final score is equal to or greater than a threshold value.
24. The automatic speaker verification method of claim 23, wherein the known speech is obtained over an enrollment channel, wherein the step of processing further comprises the step of filtering the test speech through a filter having characteristics of the enrollment channel, and wherein the step of generating subwords further comprises the step of spotting one or more key words/key phrases in the processed test speech.
training the model classifiers using antispeaker data from nonusers and one or more enrollment speech samples from the user;
changing the model classifiers and threshold value, including the step of:
determining that the user has been verified;
retraining the model classifiers, including the step of using test speech corresponding the verified final score as an enrollment sample;
calculating a new threshold value based on the retrained model classifiers.
US10042832 1996-11-22 2002-01-08 Subword-based speaker verification using multiple-classifier fusion, with channel, fusion, model and threshold adaptation Expired - Lifetime US6760701B2 (en)
US3163996 true 1996-11-22 1996-11-22
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