Victor Abrash, Michael Cohen, and Horacio Franco, SRI International, Menlo Park, CA, USA. Isao Arima, NTT Data Communications Systems Corporation, Kanagawa, Japan.
We have developed a hybrid speech recognition system which uses a multilayer perceptron (MLP) to estimate the observation likelihoods associated with the states of a HMM. In this paper, we propose two schemes for incorporating distinctive speech fea- tures (sonorant, fricative, nasal, vocalic, and voiced) into the MLP component of our system. We show a small improvement in recognition performance on a 160-word speaker-independent continuous-speech Japanese conference room reservation data- base. Further experiments simulating an improved distinctive feature classifier indicate that this approach can potentially lead to substantial performance improvements.