{"ID":2857349,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.09025","arxiv_id":"2510.09025","title":"Déréverbération non-supervisée de la parole par modèle hybride","abstract":"This paper introduces a new training strategy to improve speech dereverberation systems in an unsupervised manner using only reverberant speech. Most existing algorithms rely on paired dry/reverberant data, which is difficult to obtain. Our approach uses limited acoustic information, like the reverberation time (RT60), to train a dereverberation system. Experimental results demonstrate that our method achieves more consistent performance across various objective metrics than the state-of-the-art.","short_abstract":"This paper introduces a new training strategy to improve speech dereverberation systems in an unsupervised manner using only reverberant speech. Most existing algorithms rely on paired dry/reverberant data, which is difficult to obtain. Our approach uses limited acoustic information, like the reverberation time (RT60),...","url_abs":"https://arxiv.org/abs/2510.09025","url_pdf":"https://arxiv.org/pdf/2510.09025v1","authors":"[\"Louis Bahrman\",\"Mathieu Fontaine\",\"Gaël Richard\"]","published":"2025-10-10T05:51:17Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.AI\",\"eess.AS\"]","methods":"[]","has_code":false}