CLAP-Based Automatic Word Naming Recognition in Post-Stroke Aphasia
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866910023552073728 |
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| author | Kaloga, Yacouba Laganaro, Marina Kodrasi, Ina |
| author_facet | Kaloga, Yacouba Laganaro, Marina Kodrasi, Ina |
| contents | Conventional automatic word-naming recognition systems struggle to recognize words from post-stroke patients with aphasia because of disfluencies and mispronunciations, limiting reliable automated assessment in this population. In this paper, we propose a Contrastive Language-Audio Pretraining (CLAP) based approach for automatic word-naming recognition to address this challenge by leveraging text-audio alignment. Our approach treats word-naming recognition as an audio-text matching problem, projecting speech signals and textual prompts into a shared embedding space to identify intended words even in challenging recordings. Evaluated on two speech datasets of French post-stroke patients with aphasia, our approach achieves up to 90% accuracy, outperforming existing classification-based and automatic speech recognition-based baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14584 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CLAP-Based Automatic Word Naming Recognition in Post-Stroke Aphasia Kaloga, Yacouba Laganaro, Marina Kodrasi, Ina Audio and Speech Processing Sound Conventional automatic word-naming recognition systems struggle to recognize words from post-stroke patients with aphasia because of disfluencies and mispronunciations, limiting reliable automated assessment in this population. In this paper, we propose a Contrastive Language-Audio Pretraining (CLAP) based approach for automatic word-naming recognition to address this challenge by leveraging text-audio alignment. Our approach treats word-naming recognition as an audio-text matching problem, projecting speech signals and textual prompts into a shared embedding space to identify intended words even in challenging recordings. Evaluated on two speech datasets of French post-stroke patients with aphasia, our approach achieves up to 90% accuracy, outperforming existing classification-based and automatic speech recognition-based baselines. |
| title | CLAP-Based Automatic Word Naming Recognition in Post-Stroke Aphasia |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2602.14584 |