Few-shot short utterance speaker verification using meta-learning.

Area of Science:

• Speech processing and machine learning
• Biometric security systems

Background:

• Short utterance speaker verification (SV) relies on limited data.
• Traditional methods use deep neural networks for speaker representation.
• Meta-learning approaches learn distance metrics for speaker discrimination.

Purpose of the Study:

• To develop an effective speaker verification system for short utterances.
• To improve the learning of distinctive speaker features using meta-learning.
• To enhance the performance of prototypical networks in speaker verification tasks.

Main Methods:

• Utilized emphasized channel attention, propagation and aggregation in TDNN (ECAPA-TDNN) within a prototypical network.
• Implemented a nonlinear mapping from input to metric space for few-shot SV.
• Employed an episodic training strategy with comprehensive class representation in support and query sets.

Main Results:

• The proposed model demonstrated superior performance compared to existing methods on the VoxCeleb1 dataset.
• The integration of ECAPA-TDNN and episodic training significantly improved speaker verification accuracy.
• The system effectively handles speaker verification with minimal enrollment utterances.

Conclusions:

• The developed prototypical network with ECAPA-TDNN and episodic training is highly effective for short utterance speaker verification.
• This approach offers a robust solution for real-world SV applications requiring minimal data.
• The findings suggest broad applicability in security and authentication systems.