Abstract
In this paper, the currently popular hidden Markov modeling to speaker‐independent phoneme recognition is extended. Using multiple code books of various LPC‐derived parameters and discrete HMMs, speaker‐independent phoneme recognition accuracy of 58.8%–73.8% on the DARPA TIMIT database, depending on the type of acoustic and language models used, is obtained. In comparison, the performance of expert spectrogram readers is only 69% without use of higher level knowledge. The co‐occurrence smoothing algorithm that enables accurate recognition with only a few training examples of each phone is also introduced. Since these results were evaluated on a standard database, they can be used as benchmarks to evaluate future systems. [Work supported by DARPA.]
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