8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

Non-Linear Spectral Contrast Stretching for In-Car Speech Recognition

Weifeng Li, Hervé Bourlard

IDIAP Research Institute, Switzerland

In this paper, we present a novel feature normalization method in the log-scaled spectral domain for improving the noise robustness of speech recognition front-ends. In the proposed scheme, a non-linear contrast stretching is added to the outputs of log mel-filterbanks (MFB) to imitate the adaptation of the auditory system under adverse conditions. This is followed by a two-dimensional filter to smooth out the processing artifacts. The proposed MFCC front-ends perform remarkably well on CENSREC-2 in-car database with an average relative improvement of 29.3% compared to baseline MFCC system. It is also confirmed that the proposed processing in log MFB domain can be integrated with conventional cepstral post-processing techniques to yield further improvements. The proposed algorithm is simple and requires only a small extra computation load.

Full Paper

Bibliographic reference.  Li, Weifeng / Bourlard, Hervé (2007): "Non-linear spectral contrast stretching for in-car speech recognition", In INTERSPEECH-2007, 1122-1125.