Speaker-independent isolated word recognition using label histograms

In this paper, a simple and fast method for speaker-independent isolated word recognition is presented. This method is regarded as simplification of the approach based on the Hidden Markov Model (HMM). In the proposed method, all training and decoding data are transformed into label strings by vector quantization. By segmenting the label strings of utterances into N pieces with equal duration, label histograms are computed in the training mode. In recognition, the label string of an input word is also divided into equal N segments, and the likelihood is computed with the corresponding histogram. It will be shown that the computational cost of this method is relatively low. This method is applied to the recognition of 32-Japanese-word vocabulary, and achieved a recognition accuracy comparable to or better than that of the conventional approaches.