Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Jesus Villalba"'
Autor:
Anna Favaro, Ankur Butala, Thomas Thebaud, Jesús Villalba, Najim Dehak, Laureano Moro-Velázquez
Publikováno v:
npj Parkinson's Disease, Vol 10, Iss 1, Pp 1-17 (2024)
Abstract Numerous studies proposed methods to detect Parkinson’s disease (PD) via speech analysis. However, existing corpora often lack prodromal recordings, have small sample sizes, and lack longitudinal data. Speech samples from celebrities who p
Externí odkaz:
https://doaj.org/article/82158bc3c8b54f4faf524c79e3106109
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 30:2002-2014
Typically, unsupervised segmentation of speech into the phone and word-like units are treated as separate tasks and are often done via different methods which do not fully leverage the inter-dependence of the two tasks. Here, we unify them and propos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::375ba1579a7247c8e135c1eb934a0cd1
https://doi.org/10.1109/taslp.2022.3180684
https://doi.org/10.1109/taslp.2022.3180684
Autor:
Anna Favaro, Chelsie Motley, Tianyu Cao, Miguel Iglesias, Ankur Butala, Esther S. Oh, Robert D. Stevens, Jesus Villalba, Najim Dehak, Laureano Moro-Velazquez
Publikováno v:
2022 IEEE Spoken Language Technology Workshop (SLT).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eb4eeb1de7c599502ccefa4cb25ebaca
https://doi.org/10.1109/slt54892.2023.10022435
https://doi.org/10.1109/slt54892.2023.10022435
Publikováno v:
2022 IEEE Spoken Language Technology Workshop (SLT).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0901f0145c9b0f4d9bee6188d65f1e88
https://doi.org/10.1109/slt54892.2023.10022637
https://doi.org/10.1109/slt54892.2023.10022637
Publikováno v:
Interspeech 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41f4a1c252962f2cd686b31cfa9e44a8
https://doi.org/10.21437/interspeech.2022-11096
https://doi.org/10.21437/interspeech.2022-11096
Publikováno v:
Interspeech 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a9b5596da32a30ab329e760e8719f5d
https://doi.org/10.21437/interspeech.2022-10169
https://doi.org/10.21437/interspeech.2022-10169
Autor:
Jaejin Cho, Raghavendra Pappagari, Piotr Żelasko, Laureano Moro Velazquez, Jesus Villalba, Najim Dehak
Considering the abundance of unlabeled speech data and the high labeling costs, unsupervised learning methods can be essential for better system development. One of the most successful methods is contrastive self-supervised methods, which require neg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b719ca7bbf81db6a6466cc41c393a43
http://arxiv.org/abs/2208.05413
http://arxiv.org/abs/2208.05413
In recent studies, self-supervised pre-trained models tend to outperform supervised pre-trained models in transfer learning. In particular, self-supervised learning (SSL) of utterance-level speech representation can be used in speech applications tha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5df9139a1dbc2cb8a8508eaaa5922e58
Speech emotion recognition is the task of recognizing the speaker's emotional state given a recording of their utterance. While most of the current approaches focus on inferring emotion from isolated utterances, we argue that this is not sufficient t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97a683219f6dd658ed60d2a4c168bebf
Autor:
Anna Favaro, Laureano Moro-Velázquez, Ankur Butala, Chelsie Motley, Tianyu Cao, Robert David Stevens, Jesús Villalba, Najim Dehak
Publikováno v:
Frontiers in Neurology, Vol 14 (2023)
Motor impairments are only one aspect of Parkinson's disease (PD), which also include cognitive and linguistic impairments. Speech-derived interpretable biomarkers may help clinicians diagnose PD at earlier stages and monitor the disorder's evolution
Externí odkaz:
https://doaj.org/article/9be99e98c7b948729a6cd8c1169e215f