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Special Issue: Bringing advanced speech processing technology to the clinical management of speech disorders

International Journal of Speech-Language Pathology

International Journal of Speech-Language Pathology is an international journal which promotes discussion on a broad range of current clinical and theoretical issues. Submissions may include experimental, review and theoretical discussion papers, with studies from either quantitative and/or qualitative frameworks. Articles may relate to any area of child or adult communication or dysphagia, furthering knowledge on issues related to etiology, assessment, diagnosis, intervention, or theoretical frameworks. Articles can be accompanied by supplementary audio and video files that will be uploaded to the journal's website. Special issues on contemporary topics are published at least once a year. A scientific forum is included in many issues, where a topic is debated by invited international experts. IJSLP is the principle academic publication of Speech Pathology Australia. The journal is produced six times a year in February, April, June, August, October and December.

The potential of advanced speech processing technologies

Research to date has made it clear that many speech disorders will not be fully remediated without intensive and long-term intervention. At the same time, practitioners of speech-language pathology grapple with large caseloads and heavy documentation requirements that may leave them unable to provide an adequate duration of treatment for each client.

Advanced speech processing technologies have the potential to act as an extender of a clinician’s services by facilitating practice in between treatment sessions, or to enhance the diagnostic accuracy of their evaluations. While there has been a proliferation of apps and software to support speech intervention, few have the capacity for automated or semi-automated assessment of the quality or accuracy of the user’s speech outputs.

This special issue highlights current work that applies automatic speech recognition (ASR) or other speech processing technologies to the clinical assessment and/or treatment of disorders affecting speech production. It also discusses current limitations and barriers and identifies potential future directions for technological enhancement of the clinical management of speech disorders.

Volume 20, 2018 - Issue 6

Guest Editor: Tara McAllister
Co-editor: Kirrie J. Ballard

All articles in the special issue are free to read via this page until 31 December 2019. Bookmark now for easy access later.
Bringing advanced speech processing technology to the clinical management of speech disorders
Tara McAllister & Kirrie J. Ballard
Automated speech analysis tools for children’s speech production: A systematic literature review
J. McKechnie, B. Ahmed, R. Gutierrez-Osuna, P. Monroe, P. McCabe & K. J. Ballard
Invited Article
Automatic speech recognition: A primer for speech-language pathology researchers
Joseph Keshet
Invited Article
Automatic extraction of abnormal lip movement features from the alternating motion rate task in amyotrophic lateral sclerosis
Panying Rong, Yana Yunusova, Brian Richburg & Jordan R. Green
Invited Article
Assessing automatic VOT annotation using unimpaired and impaired speech
Esteban Buz, Adam Buchwald, Tzeviya Fuchs & Joseph Keshet
Invited Article
Selecting an acoustic correlate for automated measurement of American English rhotic production in children
Heather Campbell, Daphna Harel, Elaine Hitchcock & Tara McAllister Byun
Original Article
Speech-driven mobile games for speech therapy: User experiences and feasibility
Beena Ahmed, Penelope Monroe, Adam Hair, Chek Tien Tan, Ricardo Gutierrez-Osuna & Kirrie J. Ballard
Original Article
Assessing speech correction abilities with acoustic analyses: Evidence of preserved online correction in persons with aphasia
Caroline A. Niziolek & Swathi Kiran
Original Article
Automatic prediction of intelligible speaking rate for individuals with ALS from speech acoustic and articulatory samples
Jun Wang, Prasanna V. Kothalkar, Myungjong Kim, Andrea Bandini, Beiming Cao, Yana Yunusova, Thomas F. Campbell, Daragh Heitzman & Jordan R. Green

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