Conventional speech recognizers employ a training phase during which many of their parameters are configured - including vocabulary selection, feature selection, and decision mechanism tailoring to these selections. After this stage during normal operation, these traditional recognizers do not significantly alter any of these parameters. Conversely this work draws heavily on high level human thought patterns and speech perception to outline a set of precepts to eliminate this training phase and instead opt to perform all its tasks during the normal operation. A feature space model is discussed to establish a set of necessary and sufficient conditions to guide real-time feature selection. Detailed implementation and preliminary results are also discussed. These results indicate that benefits of this approach can be seen in increased speech recognizer adaptability while still retaining competitive recognition rates in controlled environments. Thus this can accommodate such changes as varying vocabularies, class migration, and new speakers.
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A Dynamic Vocabulary Speech Recognizer Using Real-Time, Associative-Based Learning
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