%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{ElHajal_INTERSPEECH2025_2025,
                      author = {El Hajal, Karl and Hermann, Enno and Hovsepyan, Sevada and Magimai-Doss, Mathew},
                    projects = {PaSS, IICT, EMIL},
                       month = aug,
                       title = {Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech},
                   booktitle = {Proceedings of Interspeech},
                        year = {2025},
                   publisher = {ISCA},
                    location = {Rotterdam, Netherlands},
                         url = {https://arxiv.org/abs/2506.01618},
                    abstract = {Automatic speech recognition (ASR) systems struggle with dysarthric speech due to high inter-speaker variability and slow speaking rates. To address this, we explore dysarthric-to-healthy speech conversion for improved ASR performance. Our approach extends the Rhythm and Voice (RnV) conversion framework by introducing a syllable-based rhythm modeling method suited for dysarthric speech. We assess its impact on ASR by training LF-MMI models and fine-tuning Whisper on converted speech. Experiments on the Torgo corpus reveal that LF-MMI achieves significant word error rate reductions, especially for more severe cases of dysarthria, while fine-tuning Whisper on converted data has minimal effect on its performance. These results highlight the potential of unsupervised rhythm and voice conversion for dysarthric ASR. Code available at: https://github.com/idiap/RnV.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2025/ElHajal_INTERSPEECH2025_2025.pdf}
}