AccentCL: Robust Accent Classification with Incremental Expansion
Accent classifiers are typically trained with a fixed label inventory and cannot accommodate new accent categories as new data becomes available. Moreover, accented speech corpora often exhibit substantial class imbalance and/or domain shift due to differences in recording conditions across corpora. We present AccentCL, a class-incremental learning framework for English accent classification that is robust to class imbalance and cross-corpus domain shift. AccentCL extracts multi-layer representations from a frozen Whisper-Large-v3 encoder, optimized with an imbalance-aware cross-entropy loss to reduce bias toward the majority accent classes and a domain mean alignment loss that minimizes ...
| Authors | Mu-Ruei Tseng, Waris Quamer, Ghady Nasrallah et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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