用语音识别思路做阿拉伯方言识别，适合实时流式应用。
CTC-DID: CTC-Based Arabic dialect identification for streaming applications
- 将方言识别当作有限词汇的语音识别任务处理。
- 在小数据上训练的模型优于微调的Whisper和ECAPA-TDNN。
- 对短语音更鲁棒，适合实时流式部署。
本文提出一种受连接时序分类（CTC）损失启发的阿拉伯方言识别（DID）方法。该方法将方言识别建模为一个有限词汇量的自动语音识别系统，将方言标签视为给定语音片段的标签序列。训练时，通过提出的语言无关启发式（LAH）方法或预训练的ASR模型估计转录中方言标签的重复。在低资源阿拉伯方言识别（ADI）任务上评估，实验表明基于自监督学习（SSL）的CTC-DID模型在有限数据上训练后，性能优于微调的Whisper和ECAPA-TDNN模型。值得注意的是，该方法在Casablanca数据集的零样本测试中也表现更优。此外，该方法对短语音更具鲁棒性，且易于适配流式、实时应用场景，性能衰减极小。
原文摘要 · Abstract (English)
This paper proposes a Dialect Identification (DID) approach inspired by the Connectionist Temporal Classification (CTC) loss function as used in Automatic Speech Recognition (ASR). CTC-DID frames the dialect identification task as a limited-vocabulary ASR system, where dialect tags are treated as a sequence of labels for a given utterance. For training, the repetition of dialect tags in transcriptions is estimated either using a proposed Language-Agnostic Heuristic (LAH) approach or a pre-trained ASR model. The method is evaluated on the low-resource Arabic Dialect Identification (ADI) task, with experimental results demonstrating that an SSL-based CTC-DID model, trained on a limited dataset, outperforms both fine-tuned Whisper and ECAPA-TDNN models. Notably, CTC-DID also surpasses these models in zero-shot evaluation on the Casablanca dataset. The proposed approach is found to be more robust to shorter utterances and is shown to be easily adaptable for streaming, real-time applications, with minimal performance degradation.
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