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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 19 of 19 1 Speech recognition with a hearing-aid processing scheme combining beamforming with mask-informed speech enhancement Green, Tim; Hilkhuysen, Gaston; Huckvale, Mark... In: Trends Hear (2022) BASE Show details 2 NUVA: A Naming Utterance Verifier for Aphasia Treatment ... Barbera, David Sabate; Huckvale, Mark; Fleming, Victoria. - : arXiv, 2021 BASE Show details 3 NUVA: A Naming Utterance Verifier for Aphasia Treatment Barbera, David S.; Huckvale, Mark; Fleming, Victoria; Upton, Emily; Coley-Fisher, Henry; Doogan, Catherine; Shaw, Ian; Latham, William; Leff, Alexander P.; Crinion, Jenny In: Comput Speech Lang (2021) Abstract: Anomia (word-finding difficulties) is the hallmark of aphasia, an acquired language disorder most commonly caused by stroke. Assessment of speech performance using picture naming tasks is a key method for both diagnosis and monitoring of responses to treatment interventions by people with aphasia (PWA). Currently, this assessment is conducted manually by speech and language therapists (SLT). Surprisingly, despite advancements in automatic speech recognition (ASR) and artificial intelligence with technologies like deep learning, research on developing automated systems for this task has been scarce. Here we present NUVA, an utterance verification system incorporating a deep learning element that classifies 'correct' versus' incorrect' naming attempts from aphasic stroke patients. When tested on eight native British-English speaking PWA the system's performance accuracy ranged between 83.6% to 93.6%, with a 10-fold cross-validation mean of 89.5%. This performance was not only significantly better than a baseline created for this study using one of the leading commercially available ASRs (Google speech-to-text service) but also comparable in some instances with two independent SLT ratings for the same dataset. Keyword: Article URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117974/ https://doi.org/10.1016/j.csl.2021.101221 BASE Hide details 4 Learning to control an articulatory synthesizer by imitating real speech Howard, Ian S. [Verfasser]; Huckvale, Mark A. [Verfasser]. - Frankfurt am Main : Universitätsbibliothek Johann Christian Senckenberg, 2013 DNB Subject Category Language Show details 5 Procedures used for assessment of stuttering frequency and stuttering duration Jani, Leanne; Huckvale, Mark; Howell, Peter In: Clinical linguistics & phonetics. - London : Informa Healthcare 27 (2013) 12, 853-861 OLC Linguistik Show details 6 Learning to control an articulatory synthesizer by imitating real speech Howard, Ian S.; Huckvale, Mark A. In: Speech production and perception. - Berlin : Zentrum für Allgemeine Sprachwissenschaft, Sprachtypologie und Universalienforschung 40 (2005), 63-78 UB Frankfurt Linguistik Show details 7 How is individuality expressed in voice? An introduction to speech production & description for speaker classification ... Dellwo, Volker; Huckvale, Mark; Ashby, Michael. - : Springer, 2007 BASE Show details 8 Learning to control an articulatory synthesizer by imitating natural speech Howard, Ian S.; Huckvale, Mark A. In: Speech production and perception (Berlin, 2005), p. 63-78 MPI für Psycholinguistik Show details 9 Learning to control an articulatory synthesizer by imitating natural speech Howard, Ian S.; Huckvale, Mark A. In: Zentrum für Allgemeine Sprachwissenschaft, Typologie und Universalienforschung. ZAS papers in linguistics. - Berlin : ZAS (2005) 40, 63-78 BLLDB Show details 10 Learning to control an articulatory synthesizer by imitating real speech ... Howard, Ian S.; Huckvale, Mark A.. - : ZAS Papers in Linguistics, 2005 BASE Show details 11 Improvements in speech synthesis : COST 258: the naturalness of synthetic speech Keller, Eric (Hrsg.); Bailly, Gérard (Hrsg.); Monaghan, Alex (Hrsg.). - Chichester : Wiley, 2002 BLLDB UB Frankfurt Linguistik Show details 12 Using phonologically-constrained morphological analysis in continuous speech recognition Huckvale, Mark; Fang, Alex Chengyu In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 16 (2002) 2, 165-182 OLC Linguistik Show details 13 Using phonologically-constrained morphological analysis in continuous speech recognition Huckvale, Mark; Fang, Chengyu In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 16 (2002) 2, 165-181 BLLDB Show details 14 ProSynth : an integrated prosodic approach to device-independent, natural-sounding speech synthesis Ogden, Richard; Hawkins, Sarah; House, Jill... In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 14 (2000) 3, 177-210 BLLDB Show details 15 Out-of-vocabulary rate reduction through dispersion-based lexicon acquisition Fang, Chengyu; Huckvale, Mark In: Literary & linguistic computing. - Oxford : Oxford Univ. Press 15 (2000) 3, 251-263 BLLDB Show details 16 Out-of-Vocabulary Rate Reduction through Dispersion-Based Lexicon Acquisition Fang, Alex Chengyu; Huckvale, Mark In: Literary & linguistic computing. - Oxford : Oxford Univ. Press 15 (2000) 3, 251-264 OLC Linguistik Show details 17 Studies of perceptual confusions reinterpreted as evidence for non-linear phonology Choo, Won; Huckvale, Mark In: University College London. UCL working papers in linguistics. - London : Dep. 5 (1993), 325-354 BLLDB Show details 18 Neurospeech Moore, Roger K. (Hrsg.); Wajskop, Max (Mitarb.); Huckvale, Mark (Mitarb.)... In: Speech communication. - Amsterdam [u.a.] : Elsevier 9 (1990) 1, 1-92 BLLDB Show details 19 Prosody on the Web http://www.sfs.uni-tuebingen.de/~dm/04/spring/201/pow/powin.htm Topic: Phonetics / Phonology Language: English Source type: Introductions / Tutorials Access: free access Show details

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