Robustness to speaker position in distant-talking automatic speech recognition
Randy Gómez, Keisuke Nakamura, Kazuhiro Nakadai · 2013
In this paper, we show a method that significantly improved our previous work in single-channel dereverberation. The proposed method is more robust to changes in speaker position in distant talking ASR. First, we update the room transfer function (RTF) and weighting parameters for dereverberation to the target speaker position. This scheme corrects speech power variation as a function of position in the waveform level. Consequently, its impact to the acoustic model is verified. Then, we implement a fast acoustic model update reflective of the speech power level of the target speaker position. Furthermore, the scheme in updating the model is simple and precludes time-consuming model re-estimation. As a result, the proposed method can be executed online. The synergy of these corrective measures significantly minimizes the mismatch between training and testing conditions. We test our method using real reverberant data with different locations inside the room. Experimental results show that the proposed method outperforms the conventional methods in terms of ASR performance. Moreover, our fast acoustic model update scheme is at par in terms of recognition performance against time-consuming model re-estimation.