Error-Driven Fixed-Budget ASR Personalization for Accented Speakers

Awasthi, Abhijeet; Kansal, Aman; Sarawagi, Sunita; Jyothi, Preethi

doi:10.1109/icassp39728.2021.9414830

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…The majority of these existing approaches have focused on the earlier ASR systems instead of the Deep Neural Network (DNN) based models. Although model pruning has been explored for self-supervised and other ASR models (Lai et al, 2021;Wu et al, 2021;Zhen et al, 2021) data subset selection for fine-tuning self-supervised ASR systems has only been explored in the context of personalization for accented speakers (Awasthi et al, 2021). A phoneme-level error model is proposed which selects sentences that yield a lower test WER as compared to random sentence selection.…”

Section: Related Workmentioning

confidence: 99%

Towards Representative Subset Selection for Self-Supervised Speech Recognition

Hameed¹,

Qazi²,

Raza³

2022

Preprint

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Self-supervised speech recognition models require considerable labeled training data for learning high-fidelity representations for Automatic Speech Recognition (ASR), which hinders their application to low-resource languages. We consider the task of identifying an optimal subset of training data to fine-tune self-supervised speech models for ASR. We make a surprising observation that active learning strategies for sampling harder-to-learn examples do not perform better than random subset selection for fine-tuning selfsupervised ASR. We then present the COWER-AGE algorithm for better subset selection in selfsupervised ASR which is based on our finding that ensuring the coverage of examples based on training WER in the early training epochs leads to better generalization performance. Extensive experiments on the wav2vec 2.0 model and TIMIT dataset show the effectiveness of COWERAGE, with up to 27% absolute WER improvement over active learning methods. We also report the connection between training WER and the phonemic cover and demonstrate that our algorithm ensures inclusion of phonemically diverse examples.

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Section: Related Workmentioning

confidence: 99%

Towards Representative Subset Selection for Self-Supervised Speech Recognition

Hameed¹,

Qazi²,

Raza³

2022

Preprint

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Prioritizing Speech Test Cases

Yang,

Shi,

Asyrofi

et al. 2024

ACM Trans. Softw. Eng. Methodol.

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As automated speech recognition (ASR) systems gain widespread acceptance, there is a pressing need to rigorously test and enhance their performance. Nonetheless, the process of collecting and executing speech test cases is typically both costly and time-consuming. This presents a compelling case for the strategic prioritization of speech test cases, which consist of a piece of audio and the corresponding reference text . The central question we address is: In what sequence should speech test cases be collected and executed to identify the maximum number of errors at the earliest stage? In this study, we introduce Prophet ( PR i O ritising s P eec H t E s T cases), a tool designed to predict the likelihood that speech test cases will identify errors. Consequently, Prophet can assess and prioritize these test cases without having to run the ASR system, facilitating large-scale analysis. Our evaluation encompasses \(6\) distinct prioritization techniques across \(3\) ASR systems and \(12\) datasets. When constrained by the same test budget, our approach identified \(15.44\%\) more misrecognized words than the leading the state-of-the-art method. We select top-ranked speech test cases from the prioritized list to fine-tune ASR systems and analyze how our approach can improve the ASR system performance. Statistical evaluations show that our method delivers a considerably higher performance boost for ASR systems compared to established baseline techniques. Moreover, our correlation analysis confirms that fine-tuning an ASR system with a dataset where the model initially underperforms tends to yield greater performance improvements.

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Error-Driven Fixed-Budget ASR Personalization for Accented Speakers

Cited by 2 publications

References 30 publications

Towards Representative Subset Selection for Self-Supervised Speech Recognition

Towards Representative Subset Selection for Self-Supervised Speech Recognition

Prioritizing Speech Test Cases

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