Identifying Hearing Difficulty Moments in Conversational Audio
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911085761658880 |
|---|---|
| author | Collins, Jack Buzea, Adrian Collier, Chris Rosen, Alejandro Ballesta Maclaren, Julian Lyon, Richard F. Carlile, Simon |
| author_facet | Collins, Jack Buzea, Adrian Collier, Chris Rosen, Alejandro Ballesta Maclaren, Julian Lyon, Richard F. Carlile, Simon |
| contents | Individuals regularly experience Hearing Difficulty Moments in everyday conversation. Identifying these moments of hearing difficulty has particular significance in the field of hearing assistive technology where timely interventions are key for realtime hearing assistance. In this paper, we propose and compare machine learning solutions for continuously detecting utterances that identify these specific moments in conversational audio. We show that audio language models, through their multimodal reasoning capabilities, excel at this task, significantly outperforming a simple ASR hotword heuristic and a more conventional fine-tuning approach with Wav2Vec, an audio-only input architecture that is state-of-the-art for automatic speech recognition (ASR). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23590 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identifying Hearing Difficulty Moments in Conversational Audio Collins, Jack Buzea, Adrian Collier, Chris Rosen, Alejandro Ballesta Maclaren, Julian Lyon, Richard F. Carlile, Simon Sound Audio and Speech Processing Individuals regularly experience Hearing Difficulty Moments in everyday conversation. Identifying these moments of hearing difficulty has particular significance in the field of hearing assistive technology where timely interventions are key for realtime hearing assistance. In this paper, we propose and compare machine learning solutions for continuously detecting utterances that identify these specific moments in conversational audio. We show that audio language models, through their multimodal reasoning capabilities, excel at this task, significantly outperforming a simple ASR hotword heuristic and a more conventional fine-tuning approach with Wav2Vec, an audio-only input architecture that is state-of-the-art for automatic speech recognition (ASR). |
| title | Identifying Hearing Difficulty Moments in Conversational Audio |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2507.23590 |