Improved Deep Speaker Feature Learning for Text-Dependent Speaker Recognition
Table 2: Performance with improved DNN feature learning
['[EMPTY]', 'EER% P1', 'EER% P2', 'EER% P3', 'EER% P4', 'EER% P5']
In this experiment, the phone posteriors are included in the DNN input, as shown in Fig. The phone posteriors are produced by a DNN model that was trained for ASR with a Chinese database consisting of 6000 hours of speech data. The phone set consists of 66 toneless initial and finals in Chinese, plus the silence phone. It can be seen that the phone-dependent training leads to marginal but consistent performance improvement for the d-vector system. It is clear to see that by the segmentation, significant performance is obtained. It can be observed that DTW generally outperforms segment pooling.