Deep Speaker Vectors for Semi Text-independent Speaker Verification
TABLE III: EER results on semi text-independent task
['[EMPTY]', 'PDTR', 'cosine', 'LDA', 'PLDA', 'NLDR']
It can be observed that with the simple cosine distance, the d-vector system outperforms the i-vector system in a significant way. This demonstrates that the discriminatively learned d-vectors are more discriminative for speakers when compared with the generatively learned i-vectors. However, when the discriminative normalization methods (LDA and PLDA) are employed, the performance of the i-vector system is significantly improved and better than that of the d-vector system. The discriminative methods contribute very little to the d-vector system. This is not supervising, as the d-vectors have been discriminative already. Nevertheless, the slight improvement with LDA suggests that there is some redundancy in d-vectors. Motivated by this idea, a new hidden layer with a small number of units is inserted into the DNN structure, as shown in Fig. The dimension of the new layer is set to 100, which is the best choice in our test. Compared to LDA, this approach can be regarded as a non-linear dimension reduction (NLDR). In this experiment, the phone posteriors are included in the input of the DNN structure, 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. The NLDR approach is also applied, and an additional gain is obtained.