# Phoneme-aware and Channel-wise Attentive Learning for Text DependentSpeaker Verification

arXiv: https://arxiv.org/abs/2106.13514 · HTML: https://mlsanity.com/paper/2106.13514/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [Attention-Based Models for Text-Dependent Speaker Verification](https://mlsanity.com/paper/1710.10470/) (2017)
- [Speaker Embedding Extraction with Phonetic Information](https://mlsanity.com/paper/1804.04862/) (2018)
- [Phonetic Temporal Neural Model for Language Identification](https://mlsanity.com/paper/1705.03151/) (2017)
- [Spoken Pass-Phrase Verification in the i-vector Space](https://mlsanity.com/paper/1809.11068/) (2018)

## Built on by (most cited first, 2 of 2)

- [On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification](https://mlsanity.com/paper/2201.06426/) (2022)
- [Memory-Efficient Training for Text-Dependent SV with Independent Pre-trained Models](https://mlsanity.com/paper/2411.10828/) (2024)

## Abstract (arXiv)

This paper proposes a multi-task learning network with phoneme-aware and channel-wise attentive learning strategies for text-dependent Speaker Verification (SV). In the proposed structure, the frame-level multi-task learning along with the segment-level adversarial learning is adopted for speaker embedding extraction. The phoneme-aware attentive pooling is exploited on frame-level features in the main network for speaker classifier, with the corresponding posterior probability for the phoneme distribution in the auxiliary subnet. Further, the introduction of Squeeze and Excitation (SE-block) performs dynamic channel-wise feature recalibration, which improves the representational ability. The proposed method exploits speaker idiosyncrasies associated with pass-phrases, and is further improved by the phoneme-aware attentive pooling and SE-block from temporal and channel-wise aspects, respectively. The experiments conducted on RSR2015 Part 1 database confirm that the proposed system achieves outstanding results for textdependent SV.