Abstract
Table 4.3: Speaker Verification Results on LibriSpeech test-clean-100 - Summarization with Average Pooling
['Feature', 'Feature Dim', 'Summarization', 'LDA Dim', '1st EER', '2nd EER']
After the CPC models are trained, the context vectors (hidden states) of the models are extracted as the CPC features. These features are used as the input feature for speaker verification. In the first approach, temporal average pooling, frame-level features are averaged across frames to get a fixed-length utterance-level feature for each utterance. We can first see that the speaker verification EER of the first trial list is significantly lower than that of the second trial list. This is expected since the second trial list contains no speaker-chapter overlap between enrollment and test, and thus the higher error rate. Secondly, CPC features show significant improvement over MFCC. Specifically, features from CDCK2 model recorded 5.887 and 13.48 EER, which are 36% and 18% relative improvements over the baseline. Although CDCK6 showed lower NCE loss and higher prediction accuracies during training, its features performed worse than the ones from CDCK2. We can see that i-vectors with MFCC alone got 5.518 and 8.157 EER on the two trial lists. We trained three i-vectors systems with CPC features after PCA: CDCK2-60, CDCK5-24, and CDCK6-60. We can see that these features achieved up to 11% EER relative improvement over the baseline on the first trial list. Furthermore, on the second trial list, MFCC with i-vectors prevails CPC with i-vectors. i-vectors is one of the most popular features for speech analysis tasks. It is widely used for speaker recognition, language identification, speech recognition, etc. However, one constraint that i-vectors imposed on the input feature is that it has multi-Guassian distributed. If the input feature does not comply to a multi-Guassian distribution, GMM-UBM and hence i-vectors would not likely to work. From our experiments, we observed that i-vectors is not an ideal summarization method, that summarizes frame-level feature into utterance-level feature, for CPC compared to MFCC i-vectors. CPC shows very strong results over MFCC with average pooling as the summarization method. On the other hand, when i-vectors is used as the summarization method, CPC does not show clear advantage oer MFCC. One speculation is that CPC features are not multi-Guassian distributed, and hence there may be better summarization method, such as the x-vectors, which does not assume any input distribution on the input features.