An HMM/MLP hybrid approach for improving discrimination in speech recognition

We propose an HMM/MLP hybrid scheme for achieving high discrimination in speech recognition. In the conventional hybrid approaches, an MLP is trained as a distribution estimator or as a VQ labeler, and the HMMs perform recognition using the output of the MLP. In the proposed method, to the contrary, HMMs generate a new feature vector of a fixed dimension by concatenating their state log-likelihoods, and an MLP discriminator performs recognition by using this new feature vector as an input. The proposed method was tested on the nine American E-set letters from the ISOLET database of the OGI. For comparison, a weighted HMM (WHMM) algorithm and GPD-based WHMM algorithm which use an adaptively-trained linear discriminator were also tested. In most cases, the recognition rates on the closed-test and open-test sets of the proposed method were higher than those of the conventional methods.