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pro vyhledávání: '"Ward, Nigel G."'
Autor:
Ward, Nigel G., Segura, Andres, Bugarini, Georgina, Lehnert-LeHouillier, Heike, Liu, Dancheng, Xiong, Jinjun, Fuentes, Olac
The diagnosis and treatment of individuals with communication disorders offers many opportunities for the application of speech technology, but research so far has not adequately considered: the diversity of conditions, the role of pragmatic deficits
Externí odkaz:
http://arxiv.org/abs/2409.09170
We investigate which prosodic features matter most in conveying prosodic functions. We use the problem of predicting human perceptions of pragmatic similarity among utterance pairs to evaluate the utility of prosodic features of different types. We f
Externí odkaz:
http://arxiv.org/abs/2408.13240
Autor:
Ward, Nigel G., Ortega, Carlos A.
Reduced articulatory precision is common in speech, but for dialog its acoustic properties and pragmatic functions have been little studied. We here try to remedy this gap. This technical report contains content that was omitted from the journal arti
Externí odkaz:
http://arxiv.org/abs/2405.01376
Autor:
Ward, Nigel G., Marco, Divette
Automatic measures of similarity between utterances are invaluable for training speech synthesizers, evaluating machine translation, and assessing learner productions. While there exist measures for semantic similarity and prosodic similarity, there
Externí odkaz:
http://arxiv.org/abs/2403.14808
Autor:
Avila, Jonathan E., Ward, Nigel G.
Speech-to-speech translation systems today do not adequately support use for dialog purposes. In particular, nuances of speaker intent and stance can be lost due to improper prosody transfer. We present an exploration of what needs to be done to over
Externí odkaz:
http://arxiv.org/abs/2307.04123
To support machine learning of cross-language prosodic mappings and other ways to improve speech-to-speech translation, we present a protocol for collecting closely matched pairs of utterances across languages, a description of the resulting data col
Externí odkaz:
http://arxiv.org/abs/2211.11584
Autor:
Lin, Guan-Ting, Feng, Chi-Luen, Huang, Wei-Ping, Tseng, Yuan, Lin, Tzu-Han, Li, Chen-An, Lee, Hung-yi, Ward, Nigel G.
Self-Supervised Learning (SSL) from speech data has produced models that have achieved remarkable performance in many tasks, and that are known to implicitly represent many aspects of information latently present in speech signals. However, relativel
Externí odkaz:
http://arxiv.org/abs/2210.07185
Autor:
Ward, Nigel G., Avila, Jonathan E.
Publikováno v:
In Speech Communication April 2023 149:47-62
Autor:
Marge, Matthew, Espy-Wilson, Carol, Ward, Nigel G., Alwan, Abeer, Artzi, Yoav, Bansal, Mohit, Blankenship, Gil, Chai, Joyce, Daumé, Hal, III, Dey, Debadeepta, Harper, Mary, Howard, Thomas, Kennington, Casey, Kruijff-Korbayová, Ivana, Manocha, Dinesh, Matuszek, Cynthia, Mead, Ross, Mooney, Raymond, Moore, Roger K., Ostendorf, Mari, Pon-Barry, Heather, Rudnicky, Alexander I., Scheutz, Matthias, Amant, Robert St., Sun, Tong, Tellex, Stefanie, Traum, David, Yu, Zhou
Publikováno v:
In Computer Speech & Language January 2022 71
Autor:
Niebuhr, Oliver, Ward, Nigel G.
Publikováno v:
Journal of the International Phonetic Association, 2018 Apr 01. 48(1), 1-8.
Externí odkaz:
https://www.jstor.org/stable/26502590