Source normalization training for modeling of speech

Abstract: A maximum likelihood (ML) linear regression (LR) solution to environment normalization is provided where the environment is modeled as a hidden (non-observable) variable. By application of an expectation maximization algorithm and extension of Baum-Welch forward and backward variables (Steps 23a-23d) a source normalization is achieved such that it is not necessary to label a database in terms of environment such as speaker identity, channel, microphone and noise type.

Description: This invention relates to training for H idden M arkov M odel (HMM) modeling of speech and more particularly to removing environmental factors from speech signal during the training procedure. In the present application we refer to speaker, handset or microphone, transmission channel, noise background conditions, or combination of these as the environment. A speech signal can only be measured in a particular environment. Speech recognizers suffer from environment variability because trained model distributions may be biased from testing signal distributions because environment mismatch and trained model distributions are flat because they are averaged over different environments. The first problem, the environmental mismatch, can be reduced through model adaptation, based on some utterances collected in the testing environment. To solve the second problem, the environmental factors should be removed from the speech signal during the training procedure, mainly by source normalization. In the direction of source normalization, speaker adaptive training uses linear regression (LR) solutions to decrease inter-speaker variability. See for example, T. Anastasakos, et al. entitled, "A compact model for speaker-adaptive training," International Conference on Spoken Language Processing, Vol. 2, October 1996. Another technique models mean-vectors as the sum of a speaker-independent bias and a speaker-dependent vector. This is found in A. Acero, et al. entitled, "Speaker and Gender Normalization for Continuous-Density Hidden Markov Models," in Proc. Of IEEE International Conference on Acoustics, Speech and Signal Processing, pages 342-345, Atlanta, 1996. Both of these techniques require explicit label of the classes. For example, speaker or gender of the utterance during the training. Therefore, they can not be used to train clusters of classes, which represent acoustically close speaker, hand set or microphone, or background noises. Such inability of discovering clusters may be a disadvantage in an application. An illustrative embodiment of the present invention, which is defined by the appended independent claims, seeks to provide a method for source normalization training for HMM modeling of speech that avoids or minimizes above-mentioned problems. Aspects of the invention are specified in the claims. In carrying out principles of the present invention, a method provides a maximum likelihood (ML) linear regression (LR) solution to the environment normalization problem, where the environment is modeled as a hidden (non-observable) variable. An EM-Based training algorithm can generate optimal clusters of environments and therefore it is not necessary to label a database in terms of environment. For special cases, the technique is compared to utterance-by-utterance cepstral mean normalization (CMN) technique and show performance improvement on a noisy speech telephone database. In accordance with another feature of the present invention under maximum-likelihood (ML) criterion, by application of EM algorithm and extension of Baum-Welch forward and backward variables and algorithm, a joint solution to the parameters for the source normalization is obtained, i.e. the canonical distributions, the transformations and the biases. For a better understanding of the present invention, reference will now be made, by way of example, to the accompanying drawings, in which: Fig. 1 is a block diagram of a system which includes aspects of the present invention; Fig. 2 illustrates a speech model; Fig. 3 illustrates a Gaussian distribution; Fig. 4 illustrates distortions in the distribution caused by different environments; Fig. 5 is a more detailed flow diagram of the process according to one embodiment of the present invention; and Fig. 6 is a recognizer according to an embodiment of the present invention using a source normlization model. The training is done on a computer workstation having a monitor 11, a computer workstation 13, a keyboard 15, and a mouse or other interactive device 15a, as shown in Fig. 1. The system maybe connected to a separate database represented by database 17 in Fig. 1 for storage and retrieval of models. By the term "training" we mean herein to fix the parameters of the speech models according to an optimum criterion. In this particular case, we use HMM (Hidden Markov Model) models. These models are as represented in Fig. 2 with states A, B, and C and transitions E, F, G, H, I and J between states. Each of these states has a mixture of Gaussian distributions 18 represented by Fig. 3. We are training these models to account for different environments. By environment we mean different speaker, handset, transmission channel, and noise background conditions. Speech recognizers suffer from environment variability because trained model distributions may be biased from testing signal distributions because of environment mismatch and trained model distributions are flat because they are averaged over different environments. For the first problem, the environmental mismatch can be reduced through model adaptation, based on utterances collected in the testing environment. Applicant's teaching herein is to solve the second problem by removing the environmental factors from the speech signal during the training procedure. This is source normalization training according to the present invention. A maximum likelihood (ML) linear regression (LR) solution to the environmental problem is provided herein where the environment is modeled as a hidden (non observable) variable. A clean speech pattern distribution 40 will undergo complex distortion with different environments as shown in Fig. 4. The two axes represent two parameters which may be, for example, frequency, energy, formant, spectral, or cepstral components. The Fig. 4 illustrates a change at 41 in the distribution due to background noise or a change in speakers. The purpose of the application is to model the distortion. The present model assumes the following: 1) the speech signal x is generated by Continuous Density Hidden Markov Model (CDHMM), called source distributions; 2) before being observed, the signal has undergone an environmental transformation, drawn from a set of transformations, where W je is the transformation on the HMM state j of the environment e; 3) such a transformation is linear, and is independent of the mixture components of the source; and 4) there is a bias vector b ke at the k-th mixture component due to environment e. What we observe at time t is: o t = W je x t + b ke Our problem now is to find, in the maximum likelihood (ML) sense, the optimal source distributions, the transformation and the bias set. In the prior art (A. Acero, et al. cited above and T. Anastasakos, et al. cited above), the environment e must be explicit, e.g.: speaker identity, male/female. An aspect of the present invention overcomes this limitation by allowing an arbitrary number of environments which are optimally trained. Let N be the number of HMM states, M be the mixture number, L be the number of environments, Ω s Δ {1, 2, ... N} be the set of states Ω m Δ {1, 2, ... M} be the set of mixture indicators, and Ω e Δ {1, 2, ... L} be the set of environmental indicators. For an observed speech sequence of T vectors: O Δ o T / 1 Δ (o 1 , o 2 , ... o T ), we introduce state sequence Θ Δ { o , ...  T ) where  t ∈ Ω s , mixture indicator sequence Ξ Δ (ξ 1 , ... ξ T ) where ξ t ∈ Ω m , and environment indicator sequence Φ Δ (ϕ 1 , ... ϕ T ) where ϕ t ∈ Ω e . They are all unobservable. Under some additional assumptions, the joint probability of O, Θ, Ξ, and Φ given model λ can be written as: where b jke ( o t ) Δ p ( o t | t = j ,ξ t = k ,ϕ= e , λ ) = N ( o t ; W je µ jk + b ke ,Σ jk ), u i Δ p (  1 = i ), a ij Δ p (  t +1 = j |  t = i ) c jk Δ p (ξ t = k | t = j ,λ), l e Δ p (ϕ= e |λ) Referring to Fig. 1, the workstation 13 including a processor contains a program as illustrated that starts with an initial standard HMM model 21 which is to be refined by estimation procedures using Baum-Welch or Estimation-Maximization procedures 23 to get new models 25. The program gets training data at database 19 under different environments and this is used in an iterative process to get optimal parameters. From this model we get another model 25 that takes into account environment changes. The quantities are defined by probabilities of observing a particular input vector at some particular state for a particular environment given the model. The model parameters can be determined by applying generalized EM-procedure with three types of hidden variables: state sequence, mixture component indicators, and environment indicators. (A. P. Dempster, N. M. Laird, and D. B. Rubin, entitled "Maximum Likelihood from Incomplete Data via the EM Algorithm," Journal of the Royal Statistical Society, 39 (1): 1-38, 1977.) For this purpose, Applicant teaches the CDHMM formulation from B, Juang, "Maximum-Likelihood Estimation for Mixture Multivariate Stochastic Observation of Markov Chains" ( The Bell System Technical Journal , pages 1235-1248, July-August 1985) to be extended to result in the following paragraphs: Denote: α t ( j,e ) Δ p ( o t 1 , t = j ,ϕ= e | λ ) β t ( j,e ) Δ p ( o T t +1 | t = j ,ϕ= e , λ ) γ t ( j,k,e ) Δ p (  t = j ,ξ t = k ,ϕ= e |O, λ ) The speech is observed as a sequence of frames (a vector). Equations 7, 8, and 9 are estimations of intermediate quantities. For example, in equation 7 is the joint probability of observing the frames from times 1 to t at the state j at time t and for the environment of e given the model λ. The following re-estimation equations can be derived from equations 2, 7, 8, and 9. Equation 8 is the probability of observing frames from time t+1 at the final time T given that the state at tome t is state j, that there is an environment of e, and the model λ. Equation 9 is the joint probability that the state at time t is state j, that the mixture at time t is mixture k, that there is an environment of e, given the observed frame sequence O and the model λ. For the EM procedure 23, equations 10-21 are solutions for the quantities in the model. Initial state probability: with R the number of training tokens. Transition probability: Mixture Component probability: (Mixture probability is where there is a mixture of Gaussian distributions) Environment probability: Mean vector and bias vector: We introduce: and Assuming W je = W je and for a given k, we have N+L equations: These equations 21 and 22 are solved jointly for mean vectors and bias vectors. Therefore µ jk and b ke can be simultaneously obtained by solving the linear system of N+L variables. Covariance: where δ r / t ( j,k,e ) Δ O r / t - W je µ jk -b ke Transformation: We assume covariance matrix to be diagonal: if n ≠ m . For the line m of transformation W je , we can derive (see for example C. J. Leggetter, et al., entitle "Maximum Likelihood Linear Regression for Speaker Adaptation of Continuos Density HMMs" Computer, Speech and Language , 9(2) 171-185, 1995.): Z ( m ) je =W je ( m ) R je ( m ) which is a linear system of D equations, where: Assume the means of the source distributions (µ jk ) are constant, then the above set of source normalization formulas can also be used for model adaptation. The model is specified by the parameters. The new model is specified by the new parameters. As illustrated in Figs. 1 and 5, we start with an initial as standard model 21 such as the CDHMM model with initial values. This next step is the Estimation Maximization 23 procedure starting with (Step 23a) equations 7-9 and re-estimation (Step 23b) equations 10-13 for initial state probability, transition probability, mixture component probability and environment probability. The next step (23c) is to derive a means vector and a bias vector by introducing two additional equations 14 and 15 and equation 16-20. The next step 23d is to apply linear equations 21 and 22 and solve 21 and 22 jointly for mean vectors and bias vectors and at the same time calculate the variance using equation 23. Using equation 24 which is a system of linear equations will solve for transformation parameters using quantities given by equation 25 and 26. Then we have solved for all the model parameters. Then replace the old model parameters by the newly calculated ones (Step 24). Then the process is repeated for all the frames. When this is done for all the frames of the database a new model is formed and then the new models are re-evaluated using the same equation until there is no change beyond a predetermined threshold (Step 27). After a source normalization training model is formed, this model is used in a recognizer as shown in Fig. 6 where input speech is applied to a recognizer 60 which used the source normalized HMM model 61 created by the above training to achieve the response. The recognition task has 53 commands of 1-4 words. ("call return", "cancel call return", "selective call forwarding", etc.). Utterances are recorded through telephone lines, with a diversity of microphones, including carbon, electret and cordless microphones and hands-free speaker-phones. Some of the training utterances do not correspond to their transcriptions. For example: "call screen" (cancel call screen), "matic call back" (automatic call back), "call tra" (call tracking). The speech is 8kHz sampled with 20ms frame rate. The observation vectors are composed of LPCC (Linear Prediction Coding Coefficients) derived 13-MFCC (Mel-Scale Cepstral Coefficients) plus regression based delta MFCC. CMN is performed at the utterance level. There are 3505 utterances for training and 720 for speaker-independent testing. The number of utterances per call ranges between 5-30. Because of data sparseness, besides transformation sharing among states and mixtures, the transformations need to be shared by a group of phonetically similar phones. The grouping, based on an hierarchical clustering of phones, is dependent on the amount of training (SN) or adaptation (AD) data, i.e., the larger the number of tokens is, the larger the number of transformations. Recognition experiments are run on several system configurations: BASELINE applies CMN utterance-by-utterance. This simple technique will remove channel and some long term speaker specificities, if the duration of the utterance is long enough, but can not deal with time domain additive noises. SN performs source-normalized HMM training, where the utterances of a phone-call are assumed to have been generated by a call-dependent acoustic source. Speaker, channel and background noise that are specific to the call is then removed by MLLR. An HMM recognizer is then applied using source parameters. We evaluated a special case, where each call is modeled by one environment. AD adapts traditional HMM parameters by unsupervised MLLR. 1. Using current HMMs and task grammar to phonetically recognize the test utterances, 2. Mapping the phone labels to a small number (N) of classes, which depends on the amount of data in the test utterances, 3. Estimating the LR using the N-classes and associated test data, 4. Recognizing the test utterances with transformed HMM. A similar procedure has been introduced in C. J. Legetter and P. C. Woodland. "Maximum likelihood linear regression for speaker adaptation of continuous density HMMs." Computer, Speech and Language, 9(2):171-185, 1995. SN+AD refers to AD with initial models trained by SN technique. Based on the results summarized in Table 1, we point out: For numbers of mixture components per state smaller than 16, SN, AD, and SN+AD all give consistent improvement over the baseline configuration. For numbers of mixture components per state smaller than 16, SN gives about 10% error reduction over the baseline. As SN is a training procedure which does not require any change to the recognizer, this error reduction mechanism immediately benefits applications. For all tested configurations, AD using acoustic models trained with SN procedure always gives additional error reduction. The most efficient case of SN+AD is with 32 components per state, which reduces error rate by 23%, resulting 4.64% Word Error Rate (WER) on the task. Word error rate (%) as function of test configuration and number of mixture components per state. 4 8 16 32 Baseline 7.85 6.94 6.83 5.98 SN 7.53 6.35 6.51 6.03 AD 7.15 6.41 5.61 5.87 SN+AD 6.99 6.03 5.41 4.64 Although the present invention and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the scope of the invention as defined by the appended claims.

Claims: A method of source normalization training for Hidden Markov Model (HMM) modeling of speech (Fig. 2) comprising the steps of: (a) providing an intial HMM model (21); (b) on said initial model or following a new model performing the following steps on unlabeled multi-environment training data to get a new model (25) : b1) performing (23a) expectation maximization with hidden variables of state sequence, mixture component indicators and environment indicators by estimating intermediate quantities by determining the probability of observing input speech in a frame, in a given state and given environment given a current model parameter set; and b2) performing (23b) re-expectation to determine probability in a given HMM state, probability of being in a state, probability of a given mixing component which is a Gaussian component of a mixture and probability of a given environment; b3) deriving (23c) mean vector and bias vector equations; b4) solving jointly (23d) said mean vector and bias vector equations using linear equations and by determining variances, mixing components and linear transformations for environments, solving said mean vector for said given state and said mixing component, solving said bias vector for said given environment and said mixing component; b5) replacing (24) old model parameters by the calculated ones; and (c) determining (27) after a new model is formed if it differs significantly from the previous model and if so repeating steps b1 to b5. The method of Claim 1 wherein in step b1 expectation of intermediate quantities is determined by: α t ( j,e ) Δ p ( o t / 1, t = j ,ϕ= e | λ ), the joint probability of observing input speech frames from times 1 to t in the state j and environment e, given current model parameter set λ; β t ( j,e ) Δ p ( o T / t +1| t = j , ϕ = e , λ ), the probability of observing input speech frames from time t+1 to the final time T in the state j and environment e, given current model parameter set λ; and γ t ( j,k,e ) Δ p ( t = j ,ξ t = k ,ϕ= e |O, λ ), the probability that at time t the state is j and the mixture is k in an environment e for the observed frame sequence O and current model parameter set λ. The method of Claim 2 wherein in step b2, the initial state probability is determined by transition probability is determined by mixture component probability is determined by and environment probability is determined by where R is the number of training tokens or utterances provided for generating the new model, T r is the total number of frames in the utterance r , o r / t is the observed frame t of utterance r , and is the likelihood of the utterance r given model λ . The method according to any of Claims 2 to 3 wherein step b3 deriving mean vector and bias vector is determined by the average of observed vectors at state j , mixture component k and environment e ; the joint probability of being at state j with mixture component k and in environment e , averaged over all utterances; the weighted inverse covariance matrix for mixing component k in environment e ; the transformed, by W je ' , and weighted inverse covariance matrix at state j , for mixing component k and environment e ; the transformed, by W je , and weighted, by g(j,k,e) , inverse covariance matrix at state j for mixing component k ; the scaled, by and transformed, by W je , average of observed vectors at state j and for mixture component k ; and the scaled, by average of observed vectors for mixture component k and environment e , where W je is the linear transformation of the state j in the environment e , Σ jk is the covariance matrix of the state j mixture component k , and is the inverse of Σ jk . The method according to any of Claims 2 to 4 wherein step b4 equations and are used for solving jointly Gaussian mean vectors µ jk for a state j and mixture component k , and bias vectors b ke of the mixture component k and environment e ; and equation is used to determine variance; and for diagonal covariance the equations Z ( m ) / je = W je ( m ) R je ( m ), and are used to determine transformation W je ( m ) , the line m of W je , where δ r / t ( j,k,e ) is the error vector at state j mixture component k and environment e , for the frame t of utterance r ; Z ( m,n ) / je , the n th element of the vector Z ( m ) / je , is a scaled, by and weighted, by γ, cross correlation between Gaussian mean value and error term; and R ( p,n ) / je ( m ), the element at line p and row n of the matrix R je ( m ), is a scaled, by and weighted, by γ, autocorrelation of Gaussian mean value. A speech recognition system comprising: a speech recognizer; and a source normalization model derived by application of an expectation maximization algorithm, for use with the speech recognizer, and configured so as to carry out any one of the methods of Claims 1 to 5.

Inventors: GONG YIFAN, GONG, YIFAN
Applicants: TEXAS INSTRUMENTS INC, TEXAS INSTRUMENTS INCORPORATED
Application year: 1998
Main classification: G10L15
IPC codes: G10L15