Study Reveals Performance Trade-offs and Improvement Strategies in Streaming Diarizer Small-Data Adaptation

Summary:A study on the small-data adaptation process of streaming diarizers reveals a trade-off between detection accuracy and identity consistency. While adaptation significantly improves in-domain diarization performance and transfers to an independent corpus, this improvement is inconsistent across evaluation scenarios, mainly due to impaired temporal identity consistency rather than speaker-count errors. A local-remapping diagnostic uncovers varying patterns of identity degradation across corpora, i

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Background and Motivation

Streaming diarizers are crucial for real-time speech processing, directly impacting speech recognition and understanding in multi-speaker scenarios. However, small-data adaptation, while enhancing in-domain performance, may introduce identity consistency issues. This study aims to analyze this phenomenon and explore potential improvement strategies.

Key Findings

1. Performance Improvement and Trade-offs: Small-data adaptation significantly improves in-domain diarization performance and transfers to an independent corpus. However, this improvement is inconsistent across evaluation scenarios, mainly due to impaired temporal identity consistency rather than speaker-count errors.
2. Identity Degradation Patterns: A local-remapping diagnostic reveals varying patterns of identity degradation across corpora, indicating that adaptation may alter how streaming models maintain speaker assignments over time.
3. Impact of Rehearsal: Rehearsal reduces the observed degradation but diminishes cross-domain transfer performance. This suggests the need to balance in-domain performance improvements with cross-domain migration capabilities during adaptation.

Technical Highlights

• Local-Remapping Diagnostic: A novel method for analyzing identity degradation patterns, capable of revealing specific characteristics of identity degradation across different corpora.
• Rehearsal Strategy: Reducing degradation through rehearsal, but requiring careful application to avoid impacting cross-domain migration performance.

Industry Impact and Recommendations

1. Importance of Joint Evaluation: During the adaptation of streaming diarizers, it is essential to jointly evaluate detection accuracy, identity consistency, and retention behavior to ensure overall performance improvement.
2. Optimization of Adaptation Strategies: Develop more refined adaptation strategies that enhance in-domain performance while maintaining identity consistency and cross-domain migration capabilities.
3. Expansion of Application Scenarios: Future research could explore adaptation strategies in different application scenarios, such as multilingual environments or noisy environments.

Developer Recommendations

• Focus on Identity Consistency: When designing and optimizing streaming diarizers, pay special attention to identity consistency to avoid identity degradation due to adaptation.
• Flexible Application of Rehearsal: Apply rehearsal strategies flexibly according to specific application scenarios, balancing performance improvements with cross-domain migration capabilities.

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Source: ArXiv NLP/LLM (cs.CL) (2026-10-08)

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Tags: #Streaming Diarizer Follow #Small-Data Adaptation Follow #Identity Consistency Follow #Rehearsal Follow