Hybrid segmental-LVQ/HMM for large vocabulary speech recognition - Parthasarathy, Sarangarajan; Cheng, Y. M.; O'Shaughnessy, D.; Gupta, V.; Kenny, P.; Lennig, M.; Mermelstein, P. (ICASSP 1992, 1992)
The authors have assessed the possibility of modeling phone trajectories to accomplish speech recognition. This approach has been considered as one of the ways to model context-dependency in speech recognition based on the acoustic variability of phones in the current database. A hybrid segmental learning vector quantization/hidden Markov model (SLVQ/HMM) system has been developed and evaluated on a telephone speech database. The authors have obtained 85.27% correct phrase recognition with SLVQ alone. By combining the likelihoods issued by SLVQ and by HMM, the authors have obtained 94.5% correct phrase recognition, a small improvement over that obtained with HMM alone.