An application of source coding to speaker recognition is described. The method is text-dependent - the text spoken is known, and the problem is to determine who said it. Each speaker is represented by a sequence of vector quantization codebooks; known input utterances are classified using these codebook sequences and the resulting classification distortion is compared to a rejection threshold. On a 16 speaker test population with an additional 111 imposters, this method achieved a false rejection rate of 0.8%, an imposter acceptance rate of 1.8%, and within the 16 speakers, an identification error rate of 0.0%.

A new approach to isolated word recognition is examined. This approach is based on an extension of vector quantization speech coding, called matrix quantization speech coding, that was developed by Tsao and Gray. In this new approach, a codebook containing a set of time-ordered-sequences of speech spectra represents each vocabulary word. A word is recognized by encoding it with each codebook and classifying the input word according to the codebook that yields the smallest distortion. On the digits, this approach achieved a speaker independent recognition accuracy greater than 98%. The approach is described, experimental results are presented, and comparisons with vector quantization based approaches are given.

The use of vector quantization (VQ) in isolated-word recognition of a 20-word vocabulary is examined. A separate sequence of VQ code books is designed for each word in the recognition vocabulary and input words are classified by performing VQ and finding the sequence of code books that achieve the smallest average distortion. In this paper, critical parameters are noted and the results of parameter studies are presented.

The use of vector quantization (VQ) for isolated-word speech recognition of a 20-word vocabulary was shown in previous work to achieve more than 99% accuracy for speaker-dependent recognition and 87% accuracy for speaker-independent recognition. Separate VQ codebooks were designed for each word in the recognition vocabulary, and input words were classified by performing VQ and finding the codebook that achieves the smallest average distortion. The method obviates time-normalization and makes no use of time-sequence information. This paper presents results for a generalization that incorporates time-sequence information. The generalization, which was motivated by work of Martinez, Riveria, and Buzo, is more accurate and faster than the previous method. Words in the training and input sequences are normalized linearly to the same length and then divided into sections. Separate VQ codebooks are designed for each section of each vocabulary word. Each vocabulary word is then represented by a "multisection" codebook - a time-dependent sequence of section-codebooks. New words are classified by performing VQ and finding the multi-section codebook that achieves the smallest average distortion. Initial tests on a twenty-word vocabulary resulted in accuracies greater than 97% for speaker-independent recognition.

This paper presents results of a new spectrum-analysis method that estimates a number of power spectra when a prior estimate of each is available and new information is obtained in the form of values of the auto-correlation function of their sum. The method applies for instance when one obtains autocorrelation measurements for a signal with independent additive interference, and one has prior estimates of the signal and noise spectra. By incorporating prior estimates for both spectra, the method offers considerable flexibility for tailoring an estimator to the characteristics of a signal or noise. The new method, a generalization of Minimum-Cross-Entropy Spectrum Analysis (MCESA) and Maximum Entropy Spectrum Analysis (MESA), is called Multisignal MCESA. Its theoretical basis is reviewed, and results of experimental tests of an implementation are presented. The test data comprise digitized samples of speech corrupted with helicopter noise and tone interference.

The results of a new method are presented for discrete utterance speech recognition. The method is based on rate-distortion speech coding (speech coding by vector quantization), minimum cross-entropy pattern classification, and information-theoretic spectral distortion measures. Separate vector quantization code books are designed from training sequences for each word in the recognition vocabulary. Inputs from outside the training sequence are classified by performing vector quantization and finding the code book that achieves the lowest average distortion per speech frame. The new method obviates time alignment. It achieves99percent accuracy for speaker-dependent recognition of a20-word vocabulary that includes the ten digits, with higher accuracy for recognition of the digit subset. For speaker-independent recognition, the method achieves88percent accuracy for the20-word vocabulary and95percent for the digit subset. Background of the method, detailed empirical results, and an analysis of computational requirements are presented.

We present a new, fast method for discrete utterance recognition of telephone bandwidth speech. The method is based on speech coding by vector quantization and minimum cross-entropy pattern classification. Separate vector quantization codebooks are designed from training sequences for each word in the recognition vocabulary. Inputs from outside the training sequence are classified by performing vector quantization and finding the codebook that achieves the lowest average distortion per speech frame. The new method obviates time normalization and uses approximately 6000 bits to represent each utterance in the recognition vocabulary. Preliminary limited testing on speaker dependent digit recognition has demonstrated excellent performance. Detailed tests are now in progress.