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        <title>Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition</title>
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            <namePart type="given">Mohit</namePart>
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    <abstract>Synthetic data is widely used in speech recognition due to the availability of text-to-speech models, which facilitate adapting models to previously unseen text domains. However, existing methods suffer in performance when they fine-tune an automatic speech recognition (ASR) model on synthetic data as they suffer from the distributional shift commonly referred to as the synthetic-to-real gap. In this paper, we find that task arithmetic is effective at mitigating this gap. Our proposed method, SYN2REAL task vector, shows an average improvement of 10.03% improvement in word error rate over baselines on the SLURP dataset. Additionally, we show that an average of SYN2REAL task vectors, when we have real speeches from multiple different domains, can further adapt the original ASR model to perform better on the target text domain.</abstract>
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%0 Conference Proceedings
%T Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition
%A Su, Hsuan
%A Farn, Hua
%A Sun, Fan-Yun
%A Chen, Shang-Tse
%A Lee, Hung-yi
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F su-etal-2024-task
%X Synthetic data is widely used in speech recognition due to the availability of text-to-speech models, which facilitate adapting models to previously unseen text domains. However, existing methods suffer in performance when they fine-tune an automatic speech recognition (ASR) model on synthetic data as they suffer from the distributional shift commonly referred to as the synthetic-to-real gap. In this paper, we find that task arithmetic is effective at mitigating this gap. Our proposed method, SYN2REAL task vector, shows an average improvement of 10.03% improvement in word error rate over baselines on the SLURP dataset. Additionally, we show that an average of SYN2REAL task vectors, when we have real speeches from multiple different domains, can further adapt the original ASR model to perform better on the target text domain.
%U https://aclanthology.org/2024.emnlp-main.503
%P 8905-8915

Markdown (Informal)

[Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition](https://aclanthology.org/2024.emnlp-main.503) (Su et al., EMNLP 2024)

• Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition (Su et al., EMNLP 2024)

ACL

• Hsuan Su, Hua Farn, Fan-Yun Sun, Shang-Tse Chen, and Hung-yi Lee. 2024. Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 8905–8915, Miami, Florida, USA. Association for Computational Linguistics.