Blind channel estimation based on speech correlation structure

Cepstral mean normalization is the standard technique for channel robustness. Despite its good performance, the effectiveness of cepstral mean normalization (CMN) for short sentences is argued. CMN underlying hypothesis that the speech cepstral mean is constant is not valid for short processing windows. This implies the removal of some phonetic information. In this paper we show that the speech correlation structure may be used to estimate the communication channel and we propose an efficient algorithm to compute this estimate. We argue that the resulting channel estimate is more accurate because the underlying hypothesis is better verified than the original CMN hypothesis. Results for the Kai-Fu Lee phone recognition task on NTIMIT, with acoustic models trained on TIMIT (mismatch conditions), show that our method provides an 8% relative error rate reduction as compared to CMN.