Authors: Di Cao, Xianchen Wang, Junfeng Zhou, Jiakai Zhang, Yanjing Lei, Wenpeng Chen

Published on: February 08, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.06073

Summary

• What is new: LightCAM introduces a combination of depthwise separable convolution and multi-scale feature aggregation to the Dense TDNN framework, enhancing efficiency.
• Why this is important: Traditional TDNNs are powerful for tasks like speaker verification but are computationally expensive and slow, hindering their industrial application.
• What the research proposes: The proposed LightCAM model incorporates DSM and MFA to reduce computational complexity and speed up inference while maintaining high performance.
• Results: LightCAM achieved superior performance in speaker verification on the VoxCeleb dataset with lower computational costs and faster processing times.

Technical Details

Technological frameworks used: Densely Connected Time Delay Neural Network (D-TDNN) with Context Aware Masking (CAM)

Models used: Depthwise Separable Module (DSM), Multi-Scale Feature Aggregation (MFA)

Data used: VoxCeleb dataset

Potential Impact

Voice recognition software providers, security and authentication service companies, and industries requiring efficient voice-based technologies

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