โก Quick Summary
This study introduces a novel framework called MOCA for training semi-supervised automatic speech recognition (ASR) models, effectively improving transcription accuracy for both ordinary and characterized speeches. The results demonstrate significant enhancements in performance, addressing the challenges posed by limited labeled data.
๐ Key Details
- ๐ Dataset: Real-world speech datasets
- ๐งฉ Speech Types: Ordinary speech and characterized speech
- โ๏ธ Technology: MOCA and MOCA-S frameworks
- ๐ Performance Improvement: Significant accuracy gains over previous ASR models
๐ Key Takeaways
- ๐ MOCA and MOCA-S reduce reliance on low-quality pseudo labels.
- ๐ก Multi-hypotheses approach enhances model robustness for diverse speech types.
- ๐ฃ๏ธ Characterized speech benefits from data augmentation through related speech traits.
- ๐ Extensive experiments validate the effectiveness of the proposed frameworks.
- ๐ Implications for real-world applications in transcription and translation services.
๐ Background
Automatic Speech Recognition (ASR) systems are integral to various applications, including translation and transcription services. However, the challenge of accurately transcribing characterized speechโwhich includes unique traits such as accents or speech disordersโhas been exacerbated by the limited availability of labeled data. Traditional methods often rely heavily on pseudo-labeling, which can lead to inaccuracies, particularly for characterized speech.
๐๏ธ Study
The authors of this study aimed to develop a robust framework for training ASR models that can effectively handle both ordinary and characterized speeches in a semi-supervised manner. They introduced MOCA for ordinary speech and MOCA-S for characterized speech, focusing on generating multiple hypotheses for each speech instance to mitigate the issues associated with low-quality pseudo labels.
๐ Results
The implementation of MOCA and MOCA-S led to a remarkable improvement in transcription accuracy compared to previous ASR models. The frameworks demonstrated their ability to effectively utilize limited labeled data while enhancing the model’s performance across different speech types, particularly benefiting characterized speech through trait-specific adjustments.
๐ Impact and Implications
The findings from this study have significant implications for the future of ASR technology. By addressing the challenges of low-quality pseudo labels and enhancing the model’s adaptability to various speech types, these frameworks could lead to more accurate and reliable transcription services. This advancement holds promise for improving accessibility and communication in diverse settings, including healthcare and education.
๐ฎ Conclusion
This research highlights the potential of semi-supervised learning in advancing ASR technology. The introduction of MOCA and MOCA-S represents a significant step forward in developing accurate transcription models for both ordinary and characterized speeches. As we continue to explore the capabilities of AI in speech recognition, the future looks promising for enhancing communication across various domains.
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Accurate semi-supervised automatic speech recognition for ordinary and characterized speeches via multi-hypotheses-based curriculum learning.
Abstract
How can we build accurate transcription models for both ordinary speech and characterized speech in a semi-supervised setting? ASR (Automatic Speech Recognition) systems are widely used in various real-world applications, including translation systems and transcription services. ASR models are tailored to serve one of two types of speeches: 1) ordinary speech (e.g., speeches from the general population) and 2) characterized speech (e.g., speeches from speakers with special traits, such as certain nationalities or speech disorders). Recently, the limited availability of labeled speech data and the high cost of manual labeling have drawn significant attention to the development of semi-supervised ASR systems. Previous semi-supervised ASR models employ a pseudo-labeling scheme to incorporate unlabeled examples during training. However, these methods rely heavily on pseudo labels during training and are therefore highly sensitive to the quality of pseudo labels. The issue of low-quality pseudo labels is particularly pronounced for characterized speech, due to the limited availability of data specific to a certain trait. This scarcity hinders the initial ASR model’s ability to effectively capture the unique characteristics of characterized speech, resulting in inaccurate pseudo labels. In this paper, we propose a framework for training accurate ASR models for both ordinary and characterized speeches in a semi-supervised setting. Specifically, we propose MOCA (Multi-hypotheses-based Curriculum learning for semi-supervised Asr) for ordinary speech and MOCA-S for characterized speech. MOCA and MOCA-S generate multiple hypotheses for each speech instance to reduce the heavy reliance on potentially inaccurate pseudo labels. Moreover, MOCA-S for characterized speech effectively supplements the limited trait-specific speech data by exploiting speeches of the other traits. Specifically, MOCA-S adjusts the number of pseudo labels based on the relevance to the target trait. Extensive experiments on real-world speech datasets show that MOCA and MOCA-S significantly improve the accuracy of previous ASR models.
Author: [‘Hyun Park K’, ‘Kim J’, ‘Kang U’]
Journal: PLoS One
Citation: Hyun Park K, et al. Accurate semi-supervised automatic speech recognition for ordinary and characterized speeches via multi-hypotheses-based curriculum learning. Accurate semi-supervised automatic speech recognition for ordinary and characterized speeches via multi-hypotheses-based curriculum learning. 2025; 20:e0333915. doi: 10.1371/journal.pone.0333915