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

2026-01-18

AI Summary

This work addresses the challenge of insufficient accuracy and real-time performance in Arabic dialect identification under low-resource, streaming conditions. The authors propose a limited-vocabulary speech recognition framework based on Connectionist Temporal Classification (CTC) loss, which models dialect labels as sequences of phonetic units and enables end-to-end streaming inference. This study is the first to apply CTC to dialect identification and introduces a language-agnostic heuristic label repetition strategy that significantly enhances robustness for short utterances and zero-shot scenarios. By integrating self-supervised learning (SSL) models with CTC loss and leveraging alignment labels generated via LAH or pretrained ASR systems, the proposed approach outperforms fine-tuned Whisper and ECAPA-TDNN baselines on low-resource Arabic dialect recognition tasks, achieving superior performance particularly on the Casablanca dataset in zero-shot and short-duration evaluations.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Arabic dialect identification
• streaming applications
• low-resource
• short utterances
• real-time

Innovation

• CTC-based dialect identification
• streaming speech processing
• low-resource Arabic dialects
• self-supervised learning
• zero-shot transfer