Abstract
Table 4.5: Speaker Verification Results on LibriSpeech test-clean-100 - Summarization with i-vectors
['Feature', 'Feature Dim', 'Summarization', '1st EER', '2nd EER']
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. Since MFCC and CPC are two very different feature extraction methods, they should capture different aspects of the speech signal, which may be complementary for speaker verification. We fused MFCC and CPC features before i-vectors by simply concatenating the two feature vectors. We can see that the best combinations attains 34% and 17% relative improvements over MFCC i-vectors on the two lists. 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. We observed that CPC complements MFCC for i-vectors speaker verification system. Therefore, we hypothesize that CPC complements MFCC for i-vectors based speaker verification system on the LibriSpeech corpus. However, whether this is true for all speech data is left for future work.