Visual Feedback of Tongue Movement for Novel Speech Sound Learning
Identifieur interne : 000395 ( Main/Merge ); précédent : 000394; suivant : 000396Visual Feedback of Tongue Movement for Novel Speech Sound Learning
Auteurs : William F. Katz ; Sonya MehtaSource :
- Frontiers in Human Neuroscience [ 1662-5161 ] ; 2015.
Abstract
Pronunciation training studies have yielded important information concerning the processing of audiovisual (AV) information. Second language (L2) learners show increased reliance on bottom-up, multimodal input for speech perception (compared to monolingual individuals). However, little is known about the role of viewing one's own speech articulation processes during speech training. The current study investigated whether real-time, visual feedback for tongue movement can improve a speaker's learning of non-native speech sounds. An interactive 3D tongue visualization system based on electromagnetic articulography (EMA) was used in a speech training experiment. Native speakers of American English produced a novel speech sound (/
Url:
DOI: 10.3389/fnhum.2015.00612
PubMed: 26635571
PubMed Central: 4652268
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Visual Feedback of Tongue Movement for Novel Speech Sound Learning</title><author><name sortKey="Katz, William F" sort="Katz, William F" uniqKey="Katz W" first="William F." last="Katz">William F. Katz</name></author><author><name sortKey="Mehta, Sonya" sort="Mehta, Sonya" uniqKey="Mehta S" first="Sonya" last="Mehta">Sonya Mehta</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">26635571</idno><idno type="pmc">4652268</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4652268</idno><idno type="RBID">PMC:4652268</idno><idno type="doi">10.3389/fnhum.2015.00612</idno><date when="2015">2015</date><idno type="wicri:Area/Pmc/Corpus">000177</idno><idno type="wicri:Area/Pmc/Curation">000177</idno><idno type="wicri:Area/Pmc/Checkpoint">000206</idno><idno type="wicri:Area/Ncbi/Merge">003E10</idno><idno type="wicri:Area/Ncbi/Curation">003E10</idno><idno type="wicri:Area/Ncbi/Checkpoint">003E10</idno><idno type="wicri:Area/Main/Merge">000395</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Visual Feedback of Tongue Movement for Novel Speech Sound Learning</title><author><name sortKey="Katz, William F" sort="Katz, William F" uniqKey="Katz W" first="William F." last="Katz">William F. Katz</name></author><author><name sortKey="Mehta, Sonya" sort="Mehta, Sonya" uniqKey="Mehta S" first="Sonya" last="Mehta">Sonya Mehta</name></author></analytic><series><title level="j">Frontiers in Human Neuroscience</title><idno type="eISSN">1662-5161</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Pronunciation training studies have yielded important information concerning the processing of audiovisual (AV) information. Second language (L2) learners show increased reliance on bottom-up, multimodal input for speech perception (compared to monolingual individuals). However, little is known about the role of viewing one's own speech articulation processes during speech training. The current study investigated whether real-time, visual feedback for tongue movement can improve a speaker's learning of non-native speech sounds. An interactive 3D tongue visualization system based on electromagnetic articulography (EMA) was used in a speech training experiment. Native speakers of American English produced a novel speech sound (/<underline>ɖ</underline>/; a voiced, coronal, palatal stop) before, during, and after trials in which they viewed their own speech movements using the 3D model. Talkers' productions were evaluated using kinematic (tongue-tip spatial positioning) and acoustic (burst spectra) measures. The results indicated a rapid gain in accuracy associated with visual feedback training. The findings are discussed with respect to neural models for multimodal speech processing.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Arbib, M A" uniqKey="Arbib M">M. A. Arbib</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Arnold, P" uniqKey="Arnold P">P. 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