Speaker-independent large vocabulary word recognition using an LVQ/HMM hybrid algorithm

An LVQ-HMM (learning vector quantization/hidden Markov model) hybrid algorithm was evaluated. For this evaluation, large vocabulary (5240 word) Japanese word recognition and Japanese phrase recognition were examined. Experiments in both a speaker-dependent and a speaker-independent mode were conducted. Comparison with conventional HMMs showed that LVQ-HMM improved recognition rates for words and phrases as well as for phonemes. In the speaker-dependent mode, LVQ-HMM yielded clear increases in word/phrase recognition accuracy; improvements in the recognition rates ranged between 0.8% and 4.3%. In the speaker-independent mode, however, increases in the word/phrase accuracy were small, but the results suggested some points for further study, e.g., an increase of 8% in recognition rate was achieved for one of the unknown (test) speakers, and LVQ-HMM seemed particularly effective in improving the performance for the test speakers on which conventional HMMs performed poorly