Automatic Assessment of Pronunciation Quality of Children within Assisted Speech Therapy

Schipor, Ovidiu Andrei; Pentiuc, Štefan Gheorghe; Schipor, Maria Doina

doi:10.5755/j01.eee.122.6.1813

Cited by 11 publications

(8 citation statements)

References 10 publications

(10 reference statements)

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“…Within the field of speech and language several works that use Information and Communication Technologies (ICT) have been conducted, focusing on some specific ailments [5], on the automatic classification of the quality of pronunciation when treating some disorders [6], or, on an expert system for the initial assessment of children with possible speech disorders [7]. A so-called ecosystem of smart ICTs that include electronic medical record management, standardized vocabularies, a knowledge database, ontologies for concepts within the domain of speech and language, and expert systems focused on supporting speech and language pathologists, doctors, students, patients, and their relatives can also be found [8].…”

Section: State Of the Artmentioning

confidence: 99%

A Diagnostic Tool for Speech Disorders Based on NLP with Ontological Reasoning

García¹,

González²,

Torres³

et al. 2018

RCS

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This work presents the development of an ontology for speech disorders in children, in order to become a tool to support therapists for diagnosis and possible treatment. Speech disorders are classified using a taxonomy obtained from a speech disorders corpus previously conformed. Based on this taxonomy, the ontology, which structures and formalizes concepts defined by the main topic authors, is developed. The ontology's main classes represent the taxonomic classification of speech disorders, their etiological origin, symptoms, and signs of each disorder, assessment, and intervention strategies; it also represents patients as it instances. A transcription module is also used to make different pronunciation tests and to obtain more detail of the characteristics presented by each patient to make the diagnosis. The development of the tool and the transcription module is based on Natural Language Processing (NLP) and Information Retrieval (IR) techniques. The importance of an early detection and diagnosis of a speech disorder-which can have a social, economic and educational impact-, lies in the fact that the prognosis of the treatment depends on the cause of the disorder and on an opportune treatment.

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Section: State Of the Artmentioning

confidence: 99%

A Diagnostic Tool for Speech Disorders Based on NLP with Ontological Reasoning

García¹,

González²,

Torres³

et al. 2018

RCS

View full text Add to dashboard Cite

show abstract

“…Within the field of speech and language several works that use Information and Communication Technologies (ICT) have been conducted, focusing on some ailments such as dysphagia [6], on the automatic classification of the quality of pronunciation when treating disorders such as dyslalia or dysarthria [7], or an expert system for the initial evaluation of children with possible speech disorders [8]. A so-called ecosystem of smart ICTs that include electronic medical record management, standardized vocabularies, a knowledge database, ontologies for concepts within the domain of speech and language, and expert systems focused on supporting speech and language pathologists, doctors, students, patients, and their relatives can also be found [9].…”

Section: State Of the Artmentioning

confidence: 99%

A Proposal of Lexical Resources’ Development for Ontological Learning in the Domain of Speech Disorders

Vázquez¹,

Somodevilla²,

Torres³

et al. 2017

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Speech disorders in children are a condition that could reduce the opportunity to access education, health care and in the future could mean a worse socioeconomic outcome. Therefore, early diagnosis and timely therapy is really important to reduce their impact in later stages of life. This paper presents a method for the gathering of data for a corpus related to Speech Disorders in children; such corpus will serve as the base to generate a semi-automatic ontology intended as a tool for therapists to help in the diagnosis and shape up of a therapy strategy.

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“…These disorders have motivated the application of Automatic Speech Recognition (ASR) techniques for diagnosis tasks. For example, a model for automatic assessment of pronunciation quality for children was presented by Schipor, Pentiuc, and Schipor [8], using a Hidden Markov Model (HMM) and implementing a modified correlation measure to compare the level of intelligibility of new utterances presented to the system, previously trained by experts with reference utterances. Likewise, Saz et al [9] used HMM and a subword-based pronunciation verification method to design and develop a semi-automated system to provide speech therapy.…”

Section: Speech Disorders: Dysarthria Dyslalia and Laryngeal Disordersmentioning

confidence: 99%

A Proposal of an Ecosystem Based on Intelligent Ict Tools to Support the Diagnosis and Intervention of Patients With Communication Disorders

López‐Nores¹,

García‐Duque²,

Pazos‐Arias³

et al. 2016

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Automatic Assessment of Pronunciation Quality of Children within Assisted Speech Therapy

Cited by 11 publications

References 10 publications

A Diagnostic Tool for Speech Disorders Based on NLP with Ontological Reasoning

A Diagnostic Tool for Speech Disorders Based on NLP with Ontological Reasoning

A Proposal of Lexical Resources’ Development for Ontological Learning in the Domain of Speech Disorders

A Proposal of an Ecosystem Based on Intelligent Ict Tools to Support the Diagnosis and Intervention of Patients With Communication Disorders

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