Proceedings International conference on spoken language processing
Interspeech location:Pittsburgh, PA, USA date:September, 2006
Missing Data Techniques have already shown their effectiveness
in dealing with additive noise in automatic speech recognition systems.
For real-life deployments, a compensation for linear filtering
distortions is also required. Channel compensation in speech
recognition typically involves estimating an additive shift in the
log-spectral or cepstral domain. This paper explores a maximum
likelihood technique to estimate this model offset while some data
are missing. Recognition experiments on the Aurora2 recognition
task demonstrate the effectiveness of this technique. In particular,
we show that our method is more accurate than previously
published methods and can handle narrow-band data.