Statistical modeling for automatic speech recognition
Authors: R. L. Mercer
Venue: Proceedings of the May 16-19, 1983, national computer conference (1983)

Since 1972, the Speech Recognition Group at IBM's Thomas J. Watson Research Center has worked on a system for the automatic recognition of natural continuous speech. The goal of our research is to produce a voice-activated text-processing system. We have recently worked on a 5,000-word isolated-speech system for recognition of business correspondence and interoffice memoranda. Although speech recognition is performed with beguiling ease by human beings, the task of transferring this ability to computers has proven quite difficult. Our approach to the problem has been to develop general statistical techniques that allow the recognition system to organize itself when presented with a large sample of speech called training data. In essence, our system refines itself from some crudely specified initial configuration so as to take advantage of regularities present in a speech sample. Suppose that a talker wishes to convey to a listener the string of words W1, W2, ..., Wn. Each of the words has a pronunciation, in accordance with which the talker will guide his/her speech apparatus through an intricate cascade of gestures, with the result that a series of sound pressure waves will emanate. A microphone placed within the influence of these waves will respond to them in a fashion that is characteristic of the word sequence W1, W2, ..., Wn. Let the response of the microphone be denoted by the sequence A1, ..., Am. We can regard A1, ..., Am as a garbled version of the intended message W1, ..., Wn. The problem of recovering a message from a garbled transmission is the province of communication theory. The problem may be stated as follows: for a garbled transmission A (=A1, ...Am), determine the message W (=W1, ..., Wn) that is most probable given A. That is, find W such that Pr(W|A) is as large as possible. From Bayes's rule, we have Pr(W|A) = {Pr(W|A)}Pr(W)}Pr(A). Since we are interested in maximizing this for a particular fixed A, we can ignore the denominator on the right. The first term in the numerator constitutes a probabilistic characterization of the transmission channel. It tells us what A's to expect when the talker intends a particular word string W. The second term in the numerator is a probabilistic characterization of the word strings that the talker desires to convey. Pr(W) embodies all rules of grammar (ungrammatical word strings are less probable than grammatical ones) as well as all semantic constraints (meaningless word strings are less probable than meaningful ones). It is, of course, impossible to compute these probabilities exactly. We have, however, developed methods for obtaining useful approximations to them. We refer to the approximation of Pr(A|W) as a statistical model for the speech production process and to the approximation of Pr(W) as a statistical model for the language. A discussion of these models constitutes the body of the talk. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player