Efficient ASR for Low-Resource Languages: Leveraging Cross-Lingual Unlabeled Data
Abstract
Automatic speech recognition for low-resource languages remains fundamentally constrained by the scarcity of labeled data and computational resources required by state-of-the-art models. We present a systematic investigation into cross-lingual continuous pretraining for low-resource languages, using Perso-Arabic languages (Persian, Arabic, and Urdu) as our primary case study. Our approach demonstrates that strategic utilization of unlabeled speech data can effectively bridge the resource gap without sacrificing recognition accuracy. We construct a 3,000-hour multilingual corpus through a scalable unlabeled data collection pipeline and employ targeted continual pretraining combined with morphologically-aware tokenization to develop a 300M parameter model that achieves performance comparable to systems 5 times larger. Our model outperforms Whisper Large v3 (1.5B parameters) on Persian and achieves competitive results on Arabic and Urdu despite using significantly fewer parameters and substantially less labeled data. These findings challenge the prevailing assumption that ASR quality scales primarily with model size, revealing instead that data relevance and strategic pretraining are more critical factors for low-resource scenarios. This work provides a practical pathway toward inclusive speech technology, enabling effective ASR for underrepresented languages without dependence on massive computational infrastructure or proprietary datasets.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel approach to automatic speech recognition for low-resource languages, demonstrating that strategic use of unlabeled data and targeted pretraining can yield competitive results with significantly smaller models. This research challenges existing assumptions about the relationship between model size and performance, offering a practical pathway for advancing speech technology in linguistically diverse contexts.
Methodology
The methodology presented in the paper is robust, employing a systematic approach to cross-lingual continuous pretraining and morphologically-aware tokenization. The authors construct a substantial 3,000-hour multilingual corpus and utilize a scalable data collection pipeline, which is a significant contribution to the field, especially for low-resource languages. The integration of SentencePiece tokenization is a thoughtful enhancement that addresses the unique challenges posed by the orthographic complexity of the target languages.
Results & evaluation
The experiments are well-structured, addressing clear research questions and comparing multiple pretraining strategies. The results demonstrate the effectiveness of the proposed methods, with the 300M parameter model outperforming larger models, which is a significant finding. However, the paper could benefit from additional ablation studies to further clarify the impact of each component of their approach.
Reproducibility
The authors provide a link to their codebase and model checkpoints, enhancing the reproducibility of their results. Detailed descriptions of the training procedures and hyperparameters are included, which is commendable. However, the paper lacks explicit mention of the hardware used for training, which could affect reproducibility claims.
Limitations
The paper acknowledges limitations, including the lack of multilingual joint fine-tuning and the potential inadequacy of the unlabeled corpus in representing all dialects and spontaneous speech. These limitations are important to note as they could impact the generalizability of the results in real-world applications.
Broader impact
The work has significant implications for the development of ASR systems for low-resource languages, potentially democratizing access to speech technology for underrepresented linguistic communities. By demonstrating that effective ASR can be achieved without massive datasets or computational resources, the research paves the way for more inclusive technology development. The paper presents a novel approach to automatic speech recognition for low-resource languages, demonstrating that strategic use of unlabeled data and targeted pretraining can yield competitive results with significantly smaller models. This research challenges existing assumptions about the relationship between model size and performance, offering a practical pathway for advancing speech technology in linguistically diverse contexts.
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