In this paper, we extend hidden Markov modeling to speaker-independent phone recognition. Using multiple codebooks of various LPC parameters and discrete HMMs, we obtain a speakerindependent phone recognition accuracy of 58.8% to 73.8% on the TIMTT database, depending on the type of acoustic and language models used. In comparison, the performance of expert spectrogram readers is only 69% without use of higher level knowledge. We also introduce the co-occurrence smoothing algorithm which enables accurate recognition even with very limited training data. Since our results were evaluated on a standard database, they can be used as benchmarks to evaluate future systems.

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