Make It Hard to Hear, Easy to Learn: Long-Form Bengali ASR and Speaker Diarization via Extreme Augmentation and Perfect Alignment
Abstract
Although Automatic Speech Recognition (ASR) in Bengali has seen significant progress, processing long-duration audio and performing robust speaker diarization remain critical research gaps. To address the severe scarcity of joint ASR and diarization resources for this language, we introduce Lipi-Ghor-882, a comprehensive 882-hour multi-speaker Bengali dataset. In this paper, detailing our submission to the DL Sprint 4.0 competition, we systematically evaluate various architectures and approaches for long-form Bengali speech. For ASR, we demonstrate that raw data scaling is ineffective; instead, targeted fine-tuning utilizing perfectly aligned annotations paired with synthetic acoustic degradation (noise and reverberation) emerges as the singular most effective approach. Conversely, for speaker diarization, we observed that global open-source state-of-the-art models (such as Diarizen) performed surprisingly poorly on this complex dataset. Extensive model retraining yielded negligible improvements; instead, strategic, heuristic post-processing of baseline model outputs proved to be the primary driver for increasing accuracy. Ultimately, this work outlines a highly optimized dual pipeline achieving a $\sim$0.019 Real-Time Factor (RTF), establishing a practical, empirically backed benchmark for low-resource, long-form speech processing.
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 main contribution of this paper is the introduction of the Lipi-Ghor-882 dataset and the development of optimized ASR and speaker diarization pipelines for Bengali audio. This work addresses critical gaps in the field, providing a foundation for future research and applications in low-resource speech processing. The comprehensive analysis of methodologies and results highlights the significance of targeted fine-tuning and heuristic post-processing in overcoming the challenges of long-form audio processing.
Methodology
The methodology presented in the paper is robust, focusing on two critical areas: Automatic Speech Recognition (ASR) and speaker diarization for Bengali audio. The authors systematically evaluated various architectures and fine-tuning strategies, demonstrating a clear understanding of the challenges associated with long-form audio processing. The innovative use of perfectly aligned annotations and synthetic acoustic degradation for ASR is particularly noteworthy. The shift from model retraining to heuristic post-processing for diarization reflects a pragmatic approach to overcoming the limitations of existing models in this domain.
Results & evaluation
The experiments conducted are thorough, with a clear focus on optimizing inference time and accuracy. The introduction of the Lipi-Ghor-882 dataset is a significant contribution, as it addresses the scarcity of resources for Bengali ASR and diarization. The results indicate a well-structured evaluation process, with the authors providing insights into the performance of various models and the effectiveness of their proposed methods. The achievement of a Real-Time Factor (RTF) of 0.019 is impressive and establishes a benchmark for future work.
Reproducibility
While the paper outlines the methodologies and experiments in detail, it lacks specific implementation details and code availability, which are crucial for reproducibility. The absence of a project URL or demo limits the ability of other researchers to replicate the findings or build upon this work.
Limitations
The paper acknowledges several limitations, including compute resource constraints, data quality issues, and the challenges of model optimization for Bengali features. The reliance on heuristic post-processing for diarization, while effective, indicates that further research is needed to develop more robust models for this task.
Broader impact
This research has significant implications for the field of speech processing, particularly for low-resource languages like Bengali. The introduction of a large-scale dataset and the development of optimized pipelines can facilitate advancements in conversational AI, making it more accessible for Bengali speakers. The findings may also inspire similar approaches in other low-resource language contexts. The main contribution of this paper is the introduction of the Lipi-Ghor-882 dataset and the development of optimized ASR and speaker diarization pipelines for Bengali audio. This work addresses critical gaps in the field, providing a foundation for future research and applications in low-resource speech processing. The comprehensive analysis of methodologies and results highlights the significance of targeted fine-tuning and heuristic post-processing in overcoming the challenges of long-form audio processing.
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