Lucent Technologies Inc.

Frank Kao-Ping Soong

Sunil K Gupta

Aaron Edward Rosenberg

Raziel Haimi-Cohen

Biing-Hwang Juang

Peter Kroon

Chin-Hui Lee

Suhas A Pai

Speech recognition in which the log probabilities of the null and alternative hypothesis are computed for an input speech sample by comparison with specific stored speech vocabularies/grammars and with general speech characteristics. The difference in probabilities is normalized by the magnitude of the null hypothesis to derive a likelihood factor which is compared with a rejection threshold that is utterance-length dependent. Advantageously, a high-order polynomial representation of the rejection threshold length dependency may be simplified by a series of piece-wise constants which are stored as rejection thresholds to be selected in accordance with the length of the input speech sample.

Speech recognition

In a speech recognition system, tied-mixture hidden Markov models (HMMs) are used to match, in the maximum likelihood sense, the phonemes of spoken words given the acoustic input thereof. In a well known manner, such speech recognition requires computation of state observation likelihoods (SOLs). Because of the use of HMMs, each SOL computation involves a substantial number of Gaussian kernels and mixture component weights. In accordance with the invention, the number of Gaussian kernels is cut down to reduce the computational complexity and increase the efficiency of memory access to the kernels. For example, only the non-zero mixture component weights and the Gaussian kernels associated therewith are considered in the SOL computation. In accordance with an aspect of the invention, only a subset of the Gaussian kernels of significant values, regardless of the values of the associated mixture component weights, are considered in the SOL computation. In accordance with another aspect of the invention, at least some of the mixture component weights are quantized to reduce memory space needed to store them. As such, the computational complexity and memory access efficiency are further improved.

Technique for selective use of Gaussian kernels and mixture component

In a speech recognition system for performing voice dialing, an inventive connected digit recognizer is employed to recognize a sequence of spoken digits. The inventive recognizer generates the maximum-likelihood digit sequence corresponding to the spoken sequence in accordance with the Viterbi algorithm. However, unlike a prior art connected digit recognizer, the inventive recognizer does not assume that a digit model in a sequence can be followed by any digit model with equal probability. Rather, the inventive recognizer takes into account, for each digit model being decided on, a conditional probability that that digit model would follow a given digit model preceding thereto.

Technique for effectively recognizing sequence of digits in voice dialing

The present invention provides a novel method of analyzing speech signals in order to reduce the computational power required to perform both speech compression and voice recognition operations. Digital speech signals are provided to a speech analyzer which generates a linear predictive coded (LPC) speech analysis signal that is compatible for use in both the voice recognition circuit and the speech compression circuit. The speech analysis signal is then provided to the compression circuit, which further processes the signal into a form used by an encoder and then the encoder encodes the processed signal. The same speech analysis signal is also provided to a voice recognition circuit, which further processes the signal into a form used by a recognizer and then the recognizer performs recognition on the processed signal.

Apparatus and method for speech signal analysis

A facility is provided for allowing a caller to place a telephone call by merely uttering a label identifying a desired called destination and to charge the telephone call to a particular billing account by merely uttering a label identifying that account. Alternatively, the caller may place the call by dialing or uttering the telephone number of the called destination or by entering a speed dial code associated with that telephone number. The facility includes a speaker verification system which employs cohort normalized scoring. Cohort normalized scoring provides a dynamic threshold for the verification process making the process more robust to variation in training and verification utterences. Such variation may be caused by, e.g., changes in communication channel characteristics or speaker loudness level.

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Warren, NJ, United States of America

Frank Kao-Ping Soong