This paper compares several particle filtering variants for speech feature enhancement in non-stationary noise environments. By analyzing the random processes of clean speech, noise and noisy speech, appropriate proposal densities are derived. The performances of the resulting particle filters, i.e. modified Sampling-Importance-Resampling (mod-SIR), auxiliary SIR and likelihood particle filter, are compared in terms of word accuracy achieved by the subsequent speech recognizer on the AURORA 2 database. It turns out that for the noises found in this database, noise compensation techniques that assume stationary noise work equally well.
Cite as: Haeb-Umbach, R., Schmalenstroeer, J. (2005) A comparison of particle filtering variants for speech feature enhancement. Proc. Interspeech 2005, 913-916, doi: 10.21437/Interspeech.2005-217
@inproceedings{haebumbach05b_interspeech,
author={Reinhold Haeb-Umbach and Joerg Schmalenstroeer},
title={{A comparison of particle filtering variants for speech feature enhancement}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={913--916},
doi={10.21437/Interspeech.2005-217}
}