9 / 20 / 2004 10 : 43 AM 1 Statistical Methods for Recognition Statistical Methods for the Recognition and Understanding of Speech

Statistical methods for speech processing refer to a general methodology in which knowledge about both a speech signal and the language that it expresses, along with practical uses of that knowledge for specific tasks or services, is developed from actual realizations of speech data through a well-defined mathematical and statistical formalism. For more than 20 years, this basic methodology has produced many advances and new results, particularly for recognizing and understanding speech and natural language by machine. In this article, we focus on several important statistical methods, e.g., one based primarily on the hidden Markov model (HMM) formulation that has gained widespread acceptance as the dominant technique and one related to use of statistics for characterizing word co-occurrences. In order to recognize and understand speech, the speech signal is first processed by an acoustic processor, which converts the waveform to a set of spectral feature vectors which characterize the time-varying properties of the speech sounds, and then by a linguistic decoder, which decodes the feature vectors into a word sequence which is valid according to the word lexicon and task grammar associated with the speech recognition or understanding task. The hidden Markov model approach is mainly used for acoustic modeling, that is assigning probabilities to acoustic realizations of a sequence of sounds or words, and a statistical language model is used to assign probabilities to sequences of words in the language. A Bayesian approach is used to find the word sequence with the maximum a posteriori probability over all possible sentences in the task language. This search problem is often astronomically large for large vocabulary speech understanding problems, and thus the speech-to-text decoding process often requires inordinate amounts of computing power to solve by heuristic methods. Fortunately, using results from the field of Finite State Automata Theory, we can reduce the computational burden of the search by orders of magnitude, thereby enabling exact solutions in computationally feasible times for large speech recognition problems. 1 This article is based on a series of lectures on Challenges in Speech Recognition by one of the authors (LRR) and his many colleagues at AT&T Labs Research, most especially Dr. Mazin Rahim who contributed to the presentation and figures used throughout this article. We thank Dr. Rahim for his help and support.