Handling convolutional noise in missing data automatic speech recognition

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.status: publishe