LogMax observation model with MFCC-based spectral prior for reduction of highly nonstationary ambient noise
Tomohiro Nakatani, Takuya Yoshioka, Shoko Araki, Marc Delcroix, Masakiyo Fujimoto · 2012
This paper proposes a new single/multi-channel speech enhancement approach based on a LogMax observation model integrated with Gaussian mixture models of speech and noise mel-frequency cepstral coefficients (MFCC-GMM). It has been reported that the LogMax observation model has high potential for reducing highly nonstationary noise, for example, when it is combined with factorial hidden Markov models. In addition, it has recently been shown that a source location based speech enhancement approach can be easily incorporated into this model for more efficient and reliable estimation. However, the unique structure of the LogMax model has prevented us from using it with MFCC-GMMs, which is a fundamental limitation of this approach. Our proposal in this paper is aimed at overcoming this limitation. Experiments using the PASCAL CHiME separation and recognition challenge task show the superiority of the proposed approach as regards both speech quality and automatic speech recognition performance.