{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T03:46:20Z","timestamp":1789357580396,"version":"build-2803163510"},"reference-count":21,"publisher":"Acoustical Society of America (ASA)","issue":"4","license":[{"start":{"date-parts":[[2011,9,14]],"date-time":"2011-09-14T00:00:00Z","timestamp":1315958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/pubs.aip.org\/DocumentLibrary\/files\/publications\/jasa\/jascpyrt.pdf"},{"start":{"date-parts":[[2011,9,14]],"date-time":"2011-09-14T00:00:00Z","timestamp":1315958400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/pubs.aip.org\/DocumentLibrary\/files\/publications\/jasa\/jascpyrt.pdf"}],"content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2011,10,1]]},"abstract":"<jats:p>An automatic speech recognition approach is presented which uses articulatory features estimated by a subject-independent acoustic-to-articulatory inversion. The inversion allows estimation of articulatory features from any talker\u2019s speech acoustics using only an exemplary subject\u2019s articulatory-to-acoustic map. Results are reported on a broad class phonetic classification experiment on speech from English talkers using data from three distinct English talkers as exemplars for inversion. Results indicate that the inclusion of the articulatory information improves classification accuracy but the improvement is more significant when the speaking style of the exemplar and the talker are matched compared to when they are mismatched.<\/jats:p>","DOI":"10.1121\/1.3634122","type":"journal-article","created":{"date-parts":[[2011,10,3]],"date-time":"2011-10-03T23:27:13Z","timestamp":1317684433000},"page":"EL251-EL257","update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":31,"title":["Automatic speech recognition using articulatory features from subject-independent acoustic-to-articulatory inversion"],"prefix":"10.1121","volume":"130","author":[{"given":"Prasanta Kumar","family":"Ghosh","sequence":"first","affiliation":[{"name":"University of Southern California Signal Analysis and Interpretation Laboratory, Department of Electrical Engineering, , Los Angeles, California 90089 prasantg@usc.edu , shri@sipi.usc.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shrikanth","family":"Narayanan","sequence":"additional","affiliation":[{"name":"University of Southern California Signal Analysis and Interpretation Laboratory, Department of Electrical Engineering, , Los Angeles, California 90089 prasantg@usc.edu , shri@sipi.usc.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"231","published-online":{"date-parts":[[2011,9,14]]},"reference":[{"key":"2023071823344449000_c1","first-page":"599","article-title":"ASR\u2014articulatory speech recognition","volume-title":"Proceedings of Eurospeech","year":"2001"},{"issue":"2","key":"2023071823344449000_c2","first-page":"93","article-title":"Production models as a structural basis for automatic speech recognition","volume":"22","year":"1997","journal-title":"Speech Commun."},{"key":"2023071823344449000_c3","first-page":"I","article-title":"Variational inference and learning for segmental switching state space models of hidden speech dynamics","volume-title":"Proceedings of the International Conference on Acoustics, Speech, and Signal Processing","year":"2003"},{"issue":"1","key":"2023071823344449000_c4","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TSA.2003.818074","article-title":"Target-directed mixture dynamic models for spontaneous speech recognition","volume":"12","year":"2004","journal-title":"IEEE Trans. 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