@inproceedings{parthasarathy1992hybrid,
author = {Parthasarathy, Sarangarajan and Cheng, Y. M. and O'Shaughnessy, D. and Gupta, V. and Kenny, P. and Lennig, M. and Mermelstein, P.},
title = {Hybrid segmental-LVQ/HMM for large vocabulary speech recognition},
organization = {IEEE},
booktitle = {ICASSP 1992},
year = {1992},
month = {March},
abstract = {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.},
publisher = {IEEE},
url = {http://approjects.co.za/?big=en-us/research/publication/hybrid-segmental-lvq-hmm-for-large-vocabulary-speech-recognition/},
}