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Additional file 1: of Dog vaccination with EgM proteins against Echinococcus granulosus ...
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Additional file 1: of Dog vaccination with EgM proteins against Echinococcus granulosus ...
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Automatic Prediction of Intelligible Speaking Rate for Individuals with ALS from Speech Acoustic and Articulatory Samples
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Abstract:
PURPOSE: This research aimed to automatically predict intelligible speaking rate for individuals with Amyotrophic Lateral Sclerosis (ALS) based on speech acoustic and articulatory samples. METHOD: Twelve participants with ALS and two normal subjects produced a total of 1,831 phrases. NDI Wave system was used to collect tongue and lip movement and acoustic data synchronously. A machine learning algorithm (i.e. support vector machine) was used to predict intelligible speaking rate (speech intelligibility × speaking rate) from acoustic and articulatory features of the recorded samples. RESULT: Acoustic, lip movement, and tongue movement information separately, yielded a R(2) of 0.652, 0.660, and 0.678 and a Root Mean Squared Error (RMSE) of 41.096, 41.166, and 39.855 words per minute (WPM) between the predicted and actual values, respectively. Combining acoustic, lip and tongue information we obtained the highest R(2) (0.712) and the lowest RMSE (37.562 WPM). CONCLUSION: The results revealed that our proposed analyses predicted the intelligible speaking rate of the participant with reasonably high accuracy by extracting the acoustic and/or articulatory features from one short speech sample. With further development, the analyses may be well-suited for clinical applications that require automatic speech severity prediction.
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URL: https://doi.org/10.1080/17549507.2018.1508499 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6506394/ http://www.ncbi.nlm.nih.gov/pubmed/30409057
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Using electromagnetic articulography with a tongue lateral sensor to discriminate manner of articulation
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Predicting Speech Intelligibility Decline in Amyotrophic Lateral Sclerosis Based on the Deterioration of Individual Speech Subsystems
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Humour translation in subtitling: a comparative study of the two translated version of The Big Bang Theory
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Wang, Jun. - : The University of Queensland, School of Languages and Cultures, 2017
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Recognizing Whispered Speech Produced by an Individual with Surgically Reconstructed Larynx Using Articulatory Movement Data
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Predicting Speech Intelligibility Decline in Amyotrophic Lateral Sclerosis Based on the Deterioration of Individual Speech Subsystems
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In: Special Education and Communication Disorders Faculty Publications (2016)
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Predicting Early Bulbar Decline in Amyotrophic Lateral Sclerosis: A Speech Subsystem Approach
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Predicting Early Bulbar Decline in Amyotrophic Lateral Sclerosis: A Speech Subsystem Approach
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A washback investigation of the national matriculation English test in China from the perspective of test-takers' attitudes and behaviors
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Wang, Jun. - : Sydney, Australia : Macquarie University, 2014
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A Novel DFNA36 Mutation in TMC1 Orthologous to the Beethoven (Bth) Mouse Associated with Autosomal Dominant Hearing Loss in a Chinese Family
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Preliminary Test of a Real-Time, Interactive Silent Speech Interface Based on Electromagnetic Articulograph
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In: CSE Conference and Workshop Papers (2014)
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Is the Communicative Language Teaching Approach More Effective Than the Grammar Translation Method at Teaching the Ba-Construction in Mandarin Chinese to American Undergraduate Students?
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Individual articulator's contribution to phoneme production
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In: CSE Conference and Workshop Papers (2013)
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Word Recognition from Continuous Articulatory Movement Time-Series Data using Symbolic Representations
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In: CSE Conference and Workshop Papers (2013)
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Articulatory Distinctiveness of Vowels and Consonants: A Data-Driven Approach
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In: CSE Journal Articles (2013)
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