An improved approach to the hidden Markov model decomposition of speech and noise

The author addresses the problem of automatic speech recognition in the presence of interfering noise. The novel approach described decomposes the contaminated speech signal using a generalization of standard hidden Markov modeling, while utilizing a compact and effective parametrization of the speech signal. The technique is compared to some existing noise compensation techniques, using data recorded in noise, and is found to have improved performance compared to existing model decomposition techniques. Performance is comparable to existing noise subtraction techniques, but the technique is applicable to a wider range of noise environments and is not dependent on an accurate endpointing of the speech