Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemetic Analysis

In the field of text-independent speaker recognition, dynamic models that change along the time axis have been proposed to consider the phoneme-varying characteristics of speech. However, detailed analysis on how dynamic models work depending on phonemes is insufficient. In this paper, we propose temporal dynamic CNN (TDY-CNN) that considers temporal variation of phonemes by applying kernels optimally adapt to each time bin. These kernels adapt to time bins by applying weighted sum of trained basis kernels. Then, an analysis on how adaptive kernels work on different phonemes in various layers is carried out. TDY-ResNet-38(x0.5) using six basis kernels shows better speaker verification performance than baseline model ResNet-38(x0.5) does, with an equal error rate (EER) of 1.48%. In addition, we showed that adaptive kernels depend on phoneme groups and more phoneme-specific at early layer. Temporal dynamic model adapts itself to phonemes without explicitly given phoneme information during training, and the results show that the necessity to consider phoneme variation within utterances for more accurate and robust text-independent speaker verification.