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Acoustic evidence for affix classes: A case study of Brazilian Portuguese
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In: Glossa: a journal of general linguistics; Vol 6, No 1 (2021); 21 ; 2397-1835 (2021)
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| 42 |
Strati cation without morphological strata, syllable counting without counts - modelling English stress assignment with Naive Discriminative Learning ...
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| 44 |
The iambic trochaic law in actual words: The case of English ...
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Production of nonce words to establish the cues for prominence and grouping in English ...
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| 46 |
The prosody of utterances containing the German gender asterisk ...
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| 47 |
The Iambic Trochaic Law in speech: The case of Japanese ...
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| 49 |
Numerical Study of Laryngeal Control of Phonation using Realistic Finite Element Models of a Canine Larynx
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In: Electronic Theses and Dissertations (2021)
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| 50 |
An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech
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In: Brain Sciences ; Volume 11 ; Issue 11 (2021)
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Abstract:
Childhood apraxia of speech (CAS) commonly affects the production of lexical stress contrast in polysyllabic words. Automated classification tools have the potential to increase reliability and efficiency in measuring lexical stress. Here, factors affecting the accuracy of a custom-built deep neural network (DNN)-based classification tool are evaluated. Sixteen children with typical development (TD) and 26 with CAS produced 50 polysyllabic words. Words with strong–weak (SW, e.g., dinosaur) or WS (e.g., banana) stress were fed to the classification tool, and the accuracy measured (a) against expert judgment, (b) for speaker group, and (c) with/without prior knowledge of phonemic errors in the sample. The influence of segmental features and participant factors on tool accuracy was analysed. Linear mixed modelling showed significant interaction between group and stress type, surviving adjustment for age and CAS severity. For TD, agreement for SW and WS words was > ; 80%, but CAS speech was higher for SW (> ; 80%) than WS (~60%). Prior knowledge of segmental errors conferred no clear advantage. Automatic lexical stress classification shows promise for identifying errors in children’s speech at diagnosis or with treatment-related change, but accuracy for WS words in apraxic speech needs improvement. Further training of algorithms using larger sets of labelled data containing impaired speech and WS words may increase accuracy.
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Keyword:
automatic speech recognition; childhood apraxia of speech; diagnosis; lexical stress; motor speech disorder; prosody
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URL: https://doi.org/10.3390/brainsci11111408
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| 51 |
Parenting Stress and Social Style in Mothers and Fathers of Children with Autism Spectrum Disorder: A Cross-Cultural Investigation in Italy and Japan
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In: Brain Sciences ; Volume 11 ; Issue 11 (2021)
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| 52 |
Experience of Vulnerable Women Narrated through the Body-Mapping Technique
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In: International Journal of Environmental Research and Public Health; Volume 18; Issue 24; Pages: 13094 (2021)
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| 53 |
One Year Follow up Efficacy of the Coping Power Universal and Its Relations with Teachers’ Occupational Stress
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In: Children ; Volume 8 ; Issue 10 (2021)
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| 56 |
Teaching Lexical Stress: Effective Practice in a Mandarin ELL Context
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In: Senior Honors Theses (2021)
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| 57 |
What do disyllabic words tell us about syllable structure, vowel quality, and stress in English? ...
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| 58 |
COVID-19 y la Crisis Sanitaria en Ecuador. Escenarios & Perspectivas ...
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COVID-19 y la Crisis Sanitaria en Ecuador. Escenarios & Perspectivas ...
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ВОЗДЕЙСТВИЕ ТЕХНОЛОГИЙ ВИРТУАЛЬНОЙ РЕАЛЬНОСТИ НА ПСИХОЭМОЦИОНАЛЬНОЕ СОСТОЯНИЕ ПАЦИЕНТОВ С АФАЗИЯМИ ... : The impact of virtual reality on the emotional state of patients with aphasia ...
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