CTC-DID: CTC-Based Arabic dialect identification for streaming applications

• URL: http://arxiv.org/abs/2601.12199v1
• Date: Sun, 18 Jan 2026 00:11:02 GMT
• Title: CTC-DID: CTC-Based Arabic dialect identification for streaming applications
• Authors: Muhammad Umar Farooq, Oscar Saz,
• Abstract summary: CTC-DID frames the dialect identification task as a limited-vocabulary ASR system.<n>An SSL-based CTC-DID model, trained on a limited dataset, outperforms both fine-tuned Whisper and ECAPA-TDNN models.<n>The proposed approach is found to be more robust to shorter utterances and is shown to be easily adaptable for streaming, real-time applications.
• Score: 5.548949834680307
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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