Multiband, Multisensor Robust Features for Noisy Speech Recognition

This paper presents a novel feature extraction scheme tak-ing advantage of both the nonlinear modulation speech model and the spatial diversity of speech and noise signals in a mul-tisensor environment. Herein, we propose applying robust fea-tures to speech signals captured by a multisensor array mini-mizing a noise energy criterion over multiple frequency bands. We show that we can achieve improved recognition perfor-mance by minimizing the Teager-Kaiser energy of the noise-corrupted signals in different frequency bands. These Multi-band, Multisensor Cepstral (MBSC) features are inspired by similar ones already been applied to single-microphone noisy Speech Recognition tasks with significantly improved results. The recognition results show that the proposed features can per-form better than the widely-used MFCC features