Stereo-input speech recognition using sparseness-based time-frequency masking in a reverberant environment

We present noise robust automatic speech recognition (ASR) using sparseness-based underdetermined blind source separation (BSS) technique. As a representative underdetermined BSS method, we utilized time-frequency masking in this paper. Although time-frequency masking is able to separate target speech from interferences effectively, one should consider two problems. One is that masking does not work well in noisy or reverberant environment. Another is that masking itself might cause some distortion of the target speech. For the former, we apply our time-frequency masking method [7] which can separate the target signal robustly even in noisy and reverberant environment. Next, investigating the distortion caused by time-frequency masking, we reveal following facts through experiments: 1) soft mask is better than binary mask in terms of recognition performance and 2) cepstral mean normalization (CMN) reduces the distortion, especially for that caused by soft mask. At the end, we evaluate the recognition performance of our method in noisy and reverberant real environment.