Déréverbération non-supervisée de la parole par modèle hybride
This addresses the challenge of obtaining paired dry/reverberant data for speech processing applications, though it is incremental as it builds on existing dereverberation methods.
The paper tackles the problem of speech dereverberation without paired data by introducing an unsupervised training strategy using only reverberant speech and limited acoustic information like RT60, achieving more consistent performance across objective metrics compared to state-of-the-art methods.
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.