International Business Machines Corporation

Scott Elliot Axelrod

David Nahamoo

Michael Alan Picheny

Harry William Printz

Ramesh Ambat Gopinath

George Andrei Saon

Hong-Kwang Jeff Kuo

Sreeram Viswanath Balakrishnan

Peder Andreas Olsen

Stanley S Chen

Peter V De Souza

Benoit Maison

Geoffrey G Zweig

Yuging Gao

In a speech recognition system, the combination of a log-linear model with a multitude of speech features is provided to recognize unknown speech utterances. The speech recognition system models the posterior probability of linguistic units relevant to speech recognition using a log-linear model. The posterior model captures the probability of the linguistic unit given the observed speech features and the parameters of the posterior model. The posterior model may be determined using the probability of the word sequence hypotheses given a multitude of speech features. Log-linear models are used with features derived from sparse or incomplete data. The speech features that are utilized may include asynchronous, overlapping, and statistically non-independent speech features. Not all features used in training need to appear in testing/recognition.

Speech recognition utilizing multitude of speech features

Two statistics are disclosed for determining the quality of language models. These statistics are called acoustic perplexity and the synthetic acoustic word error rate (SAWER), and they depend upon methods for computing the acoustic confusability of words. It is possible to substitute models of acoustic data in place of real acoustic data in order to determine acoustic confusability. An evaluation model is created, a synthesizer model is created, and a matrix is determined from the evaluation and synthesizer models. Each of the evaluation and synthesizer models is a hidden Markov model. Once the matrix is determined, a confusability calculation may be performed. Different methods are used to determine synthetic likelihoods. The confusability may be normalized and smoothed and methods are disclosed that increase the speed of performing the matrix inversion and the confusability calculation. A method for caching and reusing computations for similar words is disclosed. Acoustic perplexity and SAWER are determined and applied.

Determining and using acoustic confusability, acoustic perplexity and synthetic acoustic word error rate

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Mt. Kisco, NY, United States of America

Scott Elliot Axelrod