A new connected word recognition algorithm based on HMM/LVQ segmentation and LVQ classification

The authors present a novel HMM/LVQ (hidden Markov model/learning vector quantization) hybrid algorithm for connected word recognition (CWR). They show that, for combining both the discriminative power of LVQ and the capability of modeling temporal variations, of speech of an HMM into a hybrid algorithm, the performance of the original HMM-based speech recognition algorithm can be improved. The proposed hybrid algorithm is especially effective in cases when the training data are not adequate to characterize the test data. Preliminary results showed that this system gave a word accuracy of 98.5% on the whole TI test set, even when only HMM was used to segment speech utterances into words and states