Robust Distant Speech Recognition by Combining Variable-trem spectrum Based Position-dependent CMN with Conventional CMN

摘要

In a distant-talking environment, the length of channel impulse response is longer than the short-term spectral analysis window. Therefore, the conventional short-term spectrum based Cepstral Mean Normalization (CMN) is not effective under these conditions. In this paper, we propose a robust distant speech recognition method by combining a short-term spectrum based CMN with a long-term one. We assume that a static speech segment (such as a vowel, for example) affected by reverberation can be modeled by a long-term cepstral analysis. Thus, the effect of long reverberation on a static speech segment may be compensated by the long-term spectrum based CMN. In this paper, the concept of combining short-term and long-term spectrum based CMN is extended to an environmentally robust speech recognition method based on Position-Dependent CMN (PDCMN). We call this Variable Term spectrum based PDCMN (VT-PDCMN). Since PDCMN/VT-PDCMN cannot normalize speaker variations, we also combine PDCMN/VT-PDCMN with conventional CMN in this study. We conducted the experiments based on our proposed method using limited vocabulary (100 words) distant-talking isolated word recognition in a real environment. The proposed method achieved a relative error reduction rate of 60.9% over the conventional short-term spectrum based CMN and 30.6% over the short-term spectrum based PDCMN.