An Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verification (2305.12838v2)

Abstract: Effective fusion of multi-scale features is crucial for improving speaker verification performance. While most existing methods aggregate multi-scale features in a layer-wise manner via simple operations, such as summation or concatenation. This paper proposes a novel architecture called Enhanced Res2Net (ERes2Net), which incorporates both local and global feature fusion techniques to improve the performance. The local feature fusion (LFF) fuses the features within one single residual block to extract the local signal. The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal. To facilitate effective feature fusion in both LFF and GFF, an attentional feature fusion module is employed in the ERes2Net architecture, replacing summation or concatenation operations. A range of experiments conducted on the VoxCeleb datasets demonstrate the superiority of the ERes2Net in speaker verification. Code has been made publicly available at https://github.com/alibaba-damo-academy/3D-Speaker.

• The paper introduces an enhanced Res2Net that integrates multi-scale feature fusion for improved speaker verification.
• It employs a novel attentional feature fusion module to combine local details and global patterns within residual blocks.
• Experimental results show significant reductions in EER and MinDCF on VoxCeleb, outperforming conventional baseline models.

An Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verification

This paper introduces an innovative architecture named Enhanced Res2Net (ERes2Net), designed to improve speaker verification through effective multi-scale feature fusion. ERes2Net utilizes local and global feature fusion strategies to refine speaker embedding extraction, addressing limitations in existing models that employ simple feature aggregation methods such as summation or concatenation.

Architecture and Methodology

The ERes2Net architecture builds upon the traditional Res2Net structure by incorporating Local Feature Fusion (LFF) and Global Feature Fusion (GFF). These components leverage an attentional feature fusion (AFF) module that replaces conventional operations for better feature integration:

1. Local Feature Fusion (LFF): This component enhances speaker embedding discrimination at a granular level. It introduces an attentional mechanism to emphasize feature map interactions within individual residual blocks, thereby improving the network's ability to capture fine-grained local features.
2. Global Feature Fusion (GFF): GFF aggregates features from different temporal scales, enhancing the model's ability to capture global patterns. The attentional feature fusion module dynamically weights the multi-scale features, promoting robust speaker embeddings.

Experimental Results

The authors evaluated ERes2Net on the VoxCeleb datasets. The architecture exhibited superior performance over several baseline models, including Res2Net without enhancements. Notably, ERes2Net achieved significant reductions in Equal Error Rate (EER) and minimum Detection Cost Function (MinDCF) across various VoxCeleb test sets:

• On the VoxCeleb1-O test set, ERes2Net reported an EER of 0.83% and a MinDCF of 0.072 using the LMT training configuration.
• Comparative analysis with existing models, such as DF-ResNet56 and D-TDNN, demonstrated ERes2Net’s efficacy with a comparable or reduced parameter count, affirming its computational efficiency.

Implications and Future Directions

ERes2Net's advancements in speaker verification accuracy suggest promising applications in areas requiring precise speaker identification, such as security and personalized user interactions. The model's design demonstrates the utility of simultaneously capturing local details and global contexts in complex signal processing tasks.

Potential future work includes exploring the integration of ERes2Net with other deep learning paradigms, investigating alternative attention mechanisms, and adapting the model to other audio processing applications. Continued research could focus on optimizing the architecture for real-time processing or low-resource environments, broadening its practical applicability.

In conclusion, this paper offers a noteworthy contribution to speaker verification methodologies, presenting a well-conceived approach to multi-scale feature integration that advances state-of-the-art performance in the domain.