A Study on Zero-Shot Non-Intrusive Speech Intelligibility for Hearing Aids Using Large Language Models
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911136470794240 |
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| author | Zezario, Ryandhimas E. Wisnu, Dyah A. M. G. Wang, Hsin-Min Tsao, Yu |
| author_facet | Zezario, Ryandhimas E. Wisnu, Dyah A. M. G. Wang, Hsin-Min Tsao, Yu |
| contents | This work focuses on zero-shot non-intrusive speech assessment for hearing aids (HA) using large language models (LLMs). Specifically, we introduce GPT-Whisper-HA, an extension of GPT-Whisper, a zero-shot non-intrusive speech assessment model based on LLMs. GPT-Whisper-HA is designed for speech assessment for HA, incorporating MSBG hearing loss and NAL-R simulations to process audio input based on each individual's audiogram, two automatic speech recognition (ASR) modules for audio-to-text representation, and GPT-4o to predict two corresponding scores, followed by score averaging for the final estimated score. Experimental results indicate that GPT-Whisper-HA achieves a 2.59% relative root mean square error (RMSE) improvement over GPT-Whisper, confirming the potential of LLMs for zero-shot speech assessment in predicting subjective intelligibility for HA users. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03021 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Study on Zero-Shot Non-Intrusive Speech Intelligibility for Hearing Aids Using Large Language Models Zezario, Ryandhimas E. Wisnu, Dyah A. M. G. Wang, Hsin-Min Tsao, Yu Audio and Speech Processing Sound This work focuses on zero-shot non-intrusive speech assessment for hearing aids (HA) using large language models (LLMs). Specifically, we introduce GPT-Whisper-HA, an extension of GPT-Whisper, a zero-shot non-intrusive speech assessment model based on LLMs. GPT-Whisper-HA is designed for speech assessment for HA, incorporating MSBG hearing loss and NAL-R simulations to process audio input based on each individual's audiogram, two automatic speech recognition (ASR) modules for audio-to-text representation, and GPT-4o to predict two corresponding scores, followed by score averaging for the final estimated score. Experimental results indicate that GPT-Whisper-HA achieves a 2.59% relative root mean square error (RMSE) improvement over GPT-Whisper, confirming the potential of LLMs for zero-shot speech assessment in predicting subjective intelligibility for HA users. |
| title | A Study on Zero-Shot Non-Intrusive Speech Intelligibility for Hearing Aids Using Large Language Models |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2509.03021 |