Enhancement of discriminative capabilities of HMM based recognizer through modification of Viterbi algorithm

The algorithm proposed in this paper integrates the concepts of variable frame rate and discriminative analysis based on Tanimoto ratio to modify the conventional Viterbi algorithm, in such a way that the steady or stationary signal is compressed, while transitional or non-stationary signal is emphasized through the frame-by-frame searching process. The usefulness of each frame is decided entirely within the Viterbi process and needs not to be the same for different models. To evaluate this algorithm, we tested a speech database of 9 highly confusable E-set English letters. With 5 state and 6 mixture components, the conventional HMM baseline system only delivered a recognition accuracy of 73.9%. In the preliminary experiment using the algorithm proposed, the recognition accuracy was increased to 82.5%