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Automatic Assessment of Language Ability in Children with and without Typical Development.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 6111-6114, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019365
ABSTRACT
This study describes a fully automated method of expressive language assessment based on vocal responses of children to a sentence repetition task (SRT), a language test that taps into core language skills. Our proposed method automatically transcribes the vocal responses using a test-specific automatic speech recognition system. From the transcriptions, a regression model predicts the gold standard test scores provided by speech-language pathologists. Our preliminary experimental results on audio recordings of 104 children (43 with typical development and 61 with a neurodevelopmental disorder) verifies the feasibility of the proposed automatic method for predicting gold standard scores on this language test, with averaged mean absolute error of 6.52 (on a observed score range from 0 to 90 with a mean value of 49.56) between observed and predicted ratings.Clinical relevance-We describe the use of fully automatic voice-based scoring in language assessment including the clinical impact this development may have on the field of speech-language pathology. The automated test also creates a technological foundation for the computerization of a broad array of tests for voice-based language assessment.
Assuntos
Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Voz / Patologia da Fala e Linguagem Tipo de estudo: Estudo prognóstico Limite: Criança / Humanos Idioma: Inglês Revista: Annu Int Conf IEEE Eng Med Biol Soc Ano de publicação: 2020 Tipo de documento: Artigo

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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Voz / Patologia da Fala e Linguagem Tipo de estudo: Estudo prognóstico Limite: Criança / Humanos Idioma: Inglês Revista: Annu Int Conf IEEE Eng Med Biol Soc Ano de publicação: 2020 Tipo de documento: Artigo