Speaker-independent phoneme recognition using hidden Markov models
Kai-Fu Lee, Hsiao-Wuen Hon · The Journal of the Acoustical Society of America · 1988
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.]