Background noise can cause severe degradation of performance for speech recognition systems. Robustness towards background noise can be achieved by applying model-based compensation approaches. For systems that use MFCC features, the relationship between noise, speech, and resulting noise-corrupted speech is non-linear, and an important aspect of model-based approaches is how to approximate this relationship. To investigate how accurate s uch approximations need to be, in order to achieve good recognition performance, we apply three different techniques. These are evaluated on a spoken digit recognition task with artificially added noise.
Cite as: Pettersen, S.G., Johnsen, M.H., Myrvoll, T.A. (2005) A comparative study of model compensation methods for robust speech recognition in noisy conditions. Proc. Applied Spoken Language Interaction in Distributed Environments (ASIDE 2005), paper 14
@inproceedings{pettersen05_aside,
author={Svein G. Pettersen and Magne H. Johnsen and Tor A. Myrvoll},
title={{A comparative study of model compensation methods for robust speech recognition in noisy conditions}},
year=2005,
booktitle={Proc. Applied Spoken Language Interaction in Distributed Environments (ASIDE 2005)},
pages={paper 14}
}