Bengali-Loop: Community Benchmarks for Long-Form Bangla ASR and Speaker Diarization
Abstract
Bengali (Bangla) remains under-resourced in long-form speech technology despite its wide use. We present Bengali-Loop, two community benchmarks to address this gap: (1) a long-form ASR corpus of 191 recordings (158.6 hours, 792k words) from 11 YouTube channels, collected via a reproducible subtitle-extraction pipeline and human-in-the-loop transcript verification; and (2) a speaker diarization corpus of 24 recordings (22 hours, 5,744 annotated segments) with fully manual speaker-turn labels in CSV format. Both benchmarks target realistic multi-speaker, long-duration content (e.g., Bangla drama/natok). We establish baselines (Tugstugi: 34.07% WER; pyannote.audio: 40.08% DER) and provide standardized evaluation protocols (WER/CER, DER), annotation rules, and data formats to support reproducible benchmarking and future model development for Bangla long-form ASR and diarization.
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 Bengali-Loop, a significant contribution to the field of speech technology for the Bengali language, providing essential benchmarks for long-form ASR and speaker diarization. The methodology is sound, and the technical contributions are likely to foster further advancements in this under-resourced area, although some limitations and areas for improvement remain.
Methodology
The methodology presented in the paper is robust, focusing on the collection and verification of long-form ASR and speaker diarization datasets. The use of a human-in-the-loop approach for transcript verification enhances the quality of the data, addressing common pitfalls in automated transcription. The standardized evaluation protocols and formats provided are essential for reproducibility and future research. However, the paper could benefit from a more detailed discussion on the specific challenges encountered during data collection and annotation, as well as the rationale behind the chosen methodologies.
Results & evaluation
The experimental evaluation is thorough, with clear baselines established for both ASR and diarization tasks. The reported results, including WER and DER, provide a solid foundation for assessing the performance of the proposed benchmarks. However, the paper lacks a comparative analysis with existing benchmarks in other languages, which could further contextualize the results and demonstrate the significance of the contributions made.
Reproducibility
The authors emphasize reproducibility by providing detailed descriptions of the data collection process, annotation guidelines, and evaluation protocols. They also plan to release scripts for standardizing audio and running baseline evaluations, which is commendable. However, the lack of a publicly available code repository limits the ease with which other researchers can reproduce the results.
Limitations
The paper acknowledges several limitations, including the limited dialectal diversity of the datasets and the simplification of the diarization overlap policy. Additionally, the focus on specific types of media (e.g., Bangla drama) may not fully represent the diversity of spoken Bengali in other contexts. These limitations should be addressed in future work to enhance the applicability of the benchmarks.
Broader impact
The development of Bengali-Loop has significant implications for the advancement of speech technology in under-resourced languages. By providing high-quality datasets and standardized evaluation protocols, this work can facilitate further research and development in Bangla ASR and speaker diarization. The benchmarks can also serve as a foundation for community-driven efforts to improve speech technology for other low-resource languages, potentially leading to broader accessibility and inclusion in technology. The paper presents Bengali-Loop, a significant contribution to the field of speech technology for the Bengali language, providing essential benchmarks for long-form ASR and speaker diarization. The methodology is sound, and the technical contributions are likely to foster further advancements in this under-resourced area, although some limitations and areas for improvement remain.
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