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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 A Novel Interval-Valued q-Rung Dual Hesitant Linguistic Multi-Attribute Decision-Making Method Based on Linguistic Scale Functions and Power Hamy Mean Shang, Xiaopu; Feng, Xue; Wang, Jun In: Entropy (Basel) (2022) BASE Show details 2 Automatic Prediction of Intelligible Speaking Rate for Individuals with ALS from Speech Acoustic and Articulatory Samples Wang, Jun; Kothalkar, Prasanna V.; Kim, Myungjong. - 2018 BASE Show details 3 Recognizing Whispered Speech Produced by an Individual with Surgically Reconstructed Larynx Using Articulatory Movement Data Cao, Beiming; Kim, Myungjong; Mau, Ted. - 2016 BASE Show details 4 Action planning and predictive coding when speaking Wang, Jun; Mathalon, Daniel H.; Roach, Brian J.. - 2014 BASE Show details 5 Vowel Recognition from Articulatory Position Time-Series Data Wang, Jun; Samal, Ashok; Green, Jordan R.; Carrell, Tom D.. - 2009 Abstract: A new approach of recognizing vowels from articulatory position time-series data was proposed and tested in this paper. This approach directly mapped articulatory position time-series data to vowels without extracting articulatory features such as mouth opening. The input time-series data were time-normalized and sampled to fixed-width vectors of articulatory positions. Three commonly used classifiers, Neural Network, Support Vector Machine and Decision Tree were used and their performances were compared on the vectors. A single speaker dataset of eight major English vowels acquired using Electromagnetic Articulograph (EMA) AG500 was used. Recognition rate using cross validation ranged from 76.07% to 91.32% for the three classifiers. In addition, the trained decision trees were consistent with articulatory features commonly used to descriptively distinguish vowels in classical phonetics. The findings are intended to improve the accuracy and response time of a real-time articulatory-to-acoustics synthesizer. Keyword: Article URL: https://doi.org/10.1109/ICSPCS.2009.5306418 http://www.ncbi.nlm.nih.gov/pubmed/21743845 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3132171 BASE Hide details

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