通过多粒度融合提升说话人验证的细粒度特征捕捉能力
MGFF-TDNN: A Multi-Granularity Feature Fusion TDNN Model with Depth-Wise Separable Module for Speaker Verification
- 采用深度可分离卷积与多粒度融合结构，兼顾时频域细节与全局上下文
- 在VoxCeleb数据集上达到顶尖性能，参数量和计算量均保持高效
- 适合需要高精度且资源受限的实时说话人验证场景
在说话人验证中，传统模型侧重建模长时上下文特征以捕捉全局说话人特性，但常忽略包含高度判别性信息的细粒度语音指纹。本文提出一种新型模型架构MGFF-TDNN，基于多粒度特征融合。该模型使用二维深度可分离卷积模块作为前端特征提取器，增强局部特征建模能力，有效捕捉时频域特征。为实现全面的多粒度特征融合，提出M-TDNN结构，通过结合时延神经网络与音素级特征池化，同时实现全局上下文建模与细粒度特征提取。在VoxCeleb数据集上的实验表明，MGFF-TDNN在说话人验证任务中表现优异，且参数量与计算资源消耗保持高效。
原文摘要 · Abstract (English)
In speaker verification, traditional models often emphasize modeling long-term contextual features to capture global speaker characteristics. However, this approach can neglect fine-grained voiceprint information, which contains highly discriminative features essential for robust speaker embeddings. This paper introduces a novel model architecture, termed MGFF-TDNN, based on multi-granularity feature fusion. The MGFF-TDNN leverages a two-dimensional depth-wise separable convolution module, enhanced with local feature modeling, as a front-end feature extractor to effectively capture time-frequency domain features. To achieve comprehensive multi-granularity feature fusion, we propose the M-TDNN structure, which integrates global contextual modeling with fine-grained feature extraction by combining time-delay neural networks and phoneme-level feature pooling. Experiments on the VoxCeleb dataset demonstrate that the MGFF-TDNN achieves outstanding performance in speaker verification while remaining efficient in terms of parameters and computational resources.
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