In this paper, the problem of isolated word recognition in noisy environments is presented. First, the problem is solved combining the information provided by Vector Quantizers (VQs) and Hidden Markov Models (HMMs) adapted to different noise situations. And after, introducing spectral substraction and mapping between vector quantizers with and without noise as a computationally efficient technique for robust word spotting speech recognition. The results provided show high recognition rates of speech in noise, solving the classical drawbacks of previous systems.
Cite as: Ortega, J., Alvarez, J., López, E., Hernández, L. (1992) Isolated word recognition in noisy environments. Proc. ETRW on Speech Processing in Adverse Conditions, 215-218
@inproceedings{ortega92_spac,
author={J. Ortega and J. Alvarez and E. López and L. Hernández},
title={{Isolated word recognition in noisy environments}},
year=1992,
booktitle={Proc. ETRW on Speech Processing in Adverse Conditions},
pages={215--218}
}