Robustness and discrimination oriented speech recognition using weighted HMM and subspace projection approaches

Two algorithms, a weighted hidden Markov model (HMM) algorithm and a subspace projection algorithm, are proposed to address some of the discrimination and robustness issues for HMM-based speech recognition. A robust two-stage classifier is also proposed to enhance the discrimination capability of the classifiers in each of the two stages so that the overall discrimination power is improved. The proposed algorithms were evaluated using a highly confusable vocabulary consisting of the nine English E-set letters. The test was conducted in a multi-speaker, isolated-word mode. The average word accuracy for the original HMM-based system was 61.7%. When the weighted HMM and the subspace projection methods were incorporated, the word accuracy improved to 74.9% and 76.4%, respectively. By incorporating the weighted HMM in the first stage and the subspace projection in the second stage, the two-stage classifier achieved a word accuracy of 79.4%