Which Data Matter? Embedding-Based Data Selection for Speech Recognition
Abstract
Modern ASR systems are typically trained on large-scale pseudo-labeled, in-the-wild data spanning multiple domains. While such heterogeneous data benefit generalist models designed for broad deployment, they pose challenges for specialist models targeting specific domains: specialist models lack the capacity to learn from all available data, and one must pay closer attention to addressing the mismatch between training and test conditions. In this work, we study targeted data selection as a strategy to address these challenges, selecting relevant subsets from 100k hours of in-the-wild training data to optimize performance on target domains. We represent speech samples using embeddings that capture complementary characteristic--speaker attributes, phonetic content, and semantic meaning--and analyze how relevance and diversity along these axes when performing data selection affect downstream ASR performance. Our experiments with CTC-based Conformer models show that training on a strategically selected 5% subset can exceed the performance of models trained on the full dataset by up to 36.8% relative WER reduction on target domains.
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
This paper contributes to the field by proposing a robust embedding-based data selection method for ASR systems that addresses domain mismatch challenges, demonstrating significant performance improvements across various datasets. The comprehensive methodology and experimental validation provide a strong foundation for future research in data selection and ASR model training.
Methodology
The paper presents a novel approach to data selection for ASR systems using embedding-based methods that capture speaker, phonetic, and semantic characteristics. The use of Maximal Marginal Relevance (MMR) to balance relevance and diversity in data selection is a significant methodological advancement. The multi-embedding and multi-target strategies enhance the robustness of the approach, allowing for effective training on large-scale, heterogeneous datasets. The methodology is well-structured, with clear definitions and mathematical formulations that enhance clarity and reproducibility.
Results & evaluation
The experiments are comprehensive, utilizing multiple target datasets (LibriSpeech, CommonVoice, TED-LIUM) to validate the effectiveness of the proposed data selection methods. The results demonstrate substantial improvements in word error rate (WER) when using strategically selected subsets compared to random selections and the full dataset. The experiments are well-designed, with appropriate controls and comparisons that provide strong evidence for the claims made.
Reproducibility
The paper provides sufficient details regarding the implementation, including model architectures, training procedures, and data selection algorithms. However, the lack of publicly available code or datasets limits reproducibility. The use of specific embeddings and the complexity of the MMR selection process may pose challenges for others attempting to replicate the results without access to the same resources.
Limitations
The paper acknowledges the computational expense of the greedy MMR procedure and the potential for label noise in the pseudo-labeled Granary dataset. Additionally, the reliance on embedding-based selection may not generalize across all domains or datasets, and the performance may vary based on the characteristics of the target domain.
Broader impact
The findings have significant implications for the deployment of ASR systems in specialized domains, particularly in scenarios where labeled data is scarce. The ability to effectively select relevant training data can enhance the performance of models in real-world applications, making this research highly relevant to both academia and industry. The approach may also inspire further research into data selection strategies in other machine learning domains. This paper contributes to the field by proposing a robust embedding-based data selection method for ASR systems that addresses domain mismatch challenges, demonstrating significant performance improvements across various datasets. The comprehensive methodology and experimental validation provide a strong foundation for future research in data selection and ASR model training.
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