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

arXiv: https://arxiv.org/abs/2110.03213 · HTML: https://mlsanity.com/paper/2110.03213/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2021.

## Built on by (most cited first, 3 of 3)

- [Dynamic Convolutional Neural Networks as Efficient Pre-trained Audio Models](https://mlsanity.com/paper/2310.15648/) (2023)
- [Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer](https://mlsanity.com/paper/2310.17049/) (2023)
- [Dynamic Kernels and Channel Attention for Low Resource Speaker Verification](https://mlsanity.com/paper/2211.02000/) (2022)

## Abstract (arXiv)

In the field of text-independent speaker recognition, dynamic models that adapt along the time axis have been proposed to consider the phoneme-varying characteristics of speech. However, a detailed analysis of 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 adapting to each time bin. These kernels adapt to time bins by applying weighted sum of trained basis kernels. Then, an analysis of how adaptive kernels work on different phonemes in various layers is carried out. TDY-ResNet-38(x0.5) using six basis kernels improved an equal error rate (EER), the speaker verification performance, by 17.3% compared to the baseline model ResNet-38(x0.5). In addition, we showed that adaptive kernels depend on phoneme groups and are more phoneme-specific at early layers. The temporal dynamic model adapts itself to phonemes without explicitly given phoneme information during training, and results show the necessity to consider phoneme variation within utterances for more accurate and robust text-independent speaker verification.