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continuous speech recognition for medium-sized vocabularies (a few thousand words) is now possible in software on off-the-shelf workstations. Users will be able to tailor recognition capabilities to their own applications. Such software-based, real-time solutions usher in a whole new era in the development and utility of speech recognition technology.

As is often the case in technology, a paradigm shift occurs when several developments converge to make a new capability possible. In the case of continuous speech recognition, the following advances have converged to make the new technology possible:

• higher-accuracy continuous speech recognition, based on better speech modeling techniques;

• better recognition search strategies that reduce the time needed for high-accuracy recognition; and

• increased power of audio-capable, off-the-shelf workstations.

The paradigm shift is taking place in the way we view and use speech recognition. Rather than being mostly a laboratory endeavor, speech recognition is fast becoming a technology that is pervasive and will have a profound influence on the way humans communicate with machines and with each other.

This paper focuses on speech modeling advances in continuous speech recognition, with an exposition of hidden Markov models (HMMs), the mathematical backbone behind these advances. While knowledge of properties of the speech signal and of speech perception have always played a role, recent improvements have relied largely on solid mathematical and probabilistic modeling methods, especially the use of HMMs for modeling speech sounds. These methods are capable of modeling time and spectral variability simultaneously, and the model parameters can be estimated automatically from given training speech data. The traditional processes of segmentation and labeling of speech sounds are now merged into a single probabilistic process that can optimize recognition accuracy.

This paper describes the speech recognition process and provides typical recognition accuracy figures obtained in laboratory tests as a function of vocabulary, speaker dependence, grammar complexity, and the amount of speech used in training the system. As a result of modeling advances, recognition error rates have dropped several fold. Important to these improvements have been the availability of common speech corpora for training and testing purposes and the adoption of standard testing procedures.

This paper also reviews more recent research directions, including the use of segmental models and artificial neural networks in

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