Feb. 13, 2024, 5:45 a.m. | Sarthak Kumar Maharana Krishna Kamal Adidam Shoumik Nandi Ajitesh Srivastava

cs.LG updates on arXiv.org arxiv.org

Acoustic-to-articulatory inversion (AAI) involves mapping from the acoustic to the articulatory space. Signal-processing features like the MFCCs, have been widely used for the AAI task. For subjects with dysarthric speech, AAI is challenging because of an imprecise and indistinct pronunciation. In this work, we perform AAI for dysarthric speech using representations from pre-trained self-supervised learning (SSL) models. We demonstrate the impact of different pre-trained features on this challenging AAI task, at low-resource conditions. In addition, we also condition x-vectors to …

cs.lg cs.sd eess.as features mapping processing signal signal-processing space speech work

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