The purpose of this work is to improve the automatic recognition of confusable words, considering such typical examples as French and American-English Alphabets. Our study proposes a comparison between global methods like DTW or HMM and a new method using neural networks. This method is based on the search for 2 discriminative frames inside the confusable words bearing the distinction between them. Then a parametrization is done and resulting vectors are given to neural networks. The tests conducted on normal speech, Lombard speech without additive noise and Lombard speech with additive noise show a general improvement of the recognition accuracy.
Cite as: Anglade, Y., Fohr, D., Junqua, J.-C. (1992) Selectively trained neural networks for the discrimination of normal and lombard speech. Proc. 2nd International Conference on Spoken Language Processing (ICSLP 1992), 595-598, doi: 10.21437/ICSLP.1992-175
@inproceedings{anglade92_icslp,
author={Yolande Anglade and Dominique Fohr and Jean-Claude Junqua},
title={{Selectively trained neural networks for the discrimination of normal and lombard speech}},
year=1992,
booktitle={Proc. 2nd International Conference on Spoken Language Processing (ICSLP 1992)},
pages={595--598},
doi={10.21437/ICSLP.1992-175}
}