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KMID : 1022420230150030083
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Phonetics and Speech Sciences
2023 Volume.15 No. 3 p.83 ~ p.88
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Exploring the feasibility of fine-tuning large-scale speech recognition models for domain-specific applications: A case study on Whisper model and KsponSpeech dataset
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Chang Jung-Won
Nam Ho-Sung
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Abstract
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This study investigates the fine-tuning of large-scale Automatic Speech Recognition (ASR) models, specifically OpenAI’s Whisper model, for domain-specific applications using the KsponSpeech dataset. The primary research questions address the effectiveness of targeted lexical item emphasis during fine-tuning, its impact on domain-specific performance, and whether the fine-tuned model can maintain generalization capabilities across different languages and environments. Experiments were conducted using two fine-tuning datasets: Set A, a small subset emphasizing specific lexical items, and Set B, consisting of the entire KsponSpeech dataset. Results showed that fine-tuning with targeted lexical items increased recognition accuracy and improved domain-specific performance, with generalization capabilities maintained when fine-tuned with a smaller dataset. For noisier environments, a trade-off between specificity and generalization capabilities was observed. This study highlights the potential of fine-tuning using minimal domain-specific data to achieve satisfactory results, emphasizing the importance of balancing specialization and generalization for ASR models. Future research could explore different fine-tuning strategies and novel technologies such as prompting to further enhance large-scale ASR models’ domain-specific performance.
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KEYWORD
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automatic speech recognition, deep learning, Transformers, Whisper
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FullTexts / Linksout information
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Listed journal information
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