TFA-Conformer Based Network for Short Utterance Speaker Recognition

YANG Lu; ZHANG Bang-cheng; YANG Jun-mei; ZENG De-lu

doi:10.12263/DZXB.20241114

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TFA-Conformer Based Network for Short Utterance Speaker Recognition

PAPERS | 更新时间：2025-12-27

- TFA-Conformer Based Network for Short Utterance Speaker Recognition
- ACTA ELECTRONICA SINICA Vol. 53, Issue 8, Pages: 2658-2667(2025)
- 作者机构：
  
  华南理工大学电子与信息学院，广东广州 510630
- 作者简介：
- 基金信息：
  
  Natural Science Foundation of Guangdong Province(2023A1515011281)
- DOI：10.12263/DZXB.20241114
  CLC： TP391
- Received：11 December 2024，
  
  Accepted：29 May 2025，
  
  Published：25 August 2025
- 稿件说明：
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杨璐, 张邦成, 杨俊美, 等. 基于时频注意力Conformer的多尺度短语音说话人识别模型[J]. 电子学报, 2025, 53(08): 2658-2667.

YANG Lu, ZHANG Bang-cheng, YANG Jun-mei, et al. TFA-Conformer Based Network for Short Utterance Speaker Recognition[J]. Acta Electronica Sinica, 2025, 53(08): 2658-2667.
杨璐, 张邦成, 杨俊美, 等. 基于时频注意力Conformer的多尺度短语音说话人识别模型[J]. 电子学报, 2025, 53(08): 2658-2667. DOI：10.12263/DZXB.20241114

YANG Lu, ZHANG Bang-cheng, YANG Jun-mei, et al. TFA-Conformer Based Network for Short Utterance Speaker Recognition[J]. Acta Electronica Sinica, 2025, 53(08): 2658-2667. DOI：10.12263/DZXB.20241114

摘要

基于短语音的识别任务由于数据短缺、特征提取不精确，是说话人识别（Speaker Recognition，SR）领域目前面临的挑战之一.针对数据量匮乏场景下的短语音声纹特征提取和身份识别，本文设计了一种基于时频注意力和卷积增强的短语音说话人识别网络.本文在Transformer编码器中引入时频注意力和卷积，提出一种称为时频注意力Conformer（Time-Frequency Attention Convolution-augmented Transformer，TFA-Conformer）的模块，充分利用时频域通道中的信息来计算从全局到局部的有效性权重，帮助模型捕获精确的声学特征，使得特征编码器在短语音（3 s以内）环境下生成具有高判别性的说话人特征向量.本文在标准说话人数据集TIMIT和ST-CMDS上评估了所提出的有监督训练网络模型，在短语音条件下，其识别准确性等指标相比主流方法平均提升4.837%，并且在更短时间和更少数据量的语音段识别中有平均2.799%的相对提升.本文提出模型的参数更少且计算复杂度更低，其适用于短语音场景的同时也更轻量化.

Abstract

The recognition task based on short utterances is one of the challenges in the field of speaker recognition (SR) due to data scarcity and inaccurate feature extraction. In scenarios with limited data

this paper proposes a short utterance speaker recognition network based on time-frequency (T-F) attention and convolutional enhancement for feature extraction and identity recognition. We introduce a time-frequency attention module and a convolution module in the transformer encoder to propose a module called time-frequency attention conformer (TFA-Conformer)

which helps the model capture precise acoustic features by utilizing information from T-F channels to calculate validity weights from global to local perspectives

thereby enabling the feature encoder to produce highly discriminative speaker embeddings under short utterance speech conditions (3 s or less). We evaluate the proposed supervised training network on datasets under short utterance conditions

and the recognition accuracy and other metrics of the proposed method are improved by 4.837% on average

higher than those of the mainstream methods. In condition with shorter duration and less data

the proposed method shows a relative improvement of 2.799% on average. Furthermore

it requires fewer parameters and lower computational complexity

making it not only suitable for short utterance scenarios but also more lightweight.

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Keywords

references

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