A learning procedure for speaker-dependent word recognition systems based on sequential processing of input tokens

This paper Presents a learning procedure for speaker-dependent word recognition systems which are based on the principle of dynamic time warping. The reference templates are created by averaging word tokens for each class. The averaging, procedure, which is based on a purely sequential processing of the tokens, contains additional weighting operations for word boundaries and scaling of the time axis. These operations improve the robustness of the learning procedure. The new learning procedure has been tested with different speech examples, some of which have been recorded in extremely noisy conditions with casual speakers. In all cases the learning procedure yields very reliable reference templates.