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Full-Text Articles in Medicine and Health Sciences
Parallel Reference Speaker Weighting For Kinematic-Independent Acoustic-To-Articulatory Inversion, An Ji, Michael T. Johnson, Jeffrey J. Berry
Parallel Reference Speaker Weighting For Kinematic-Independent Acoustic-To-Articulatory Inversion, An Ji, Michael T. Johnson, Jeffrey J. Berry
Speech Pathology and Audiology Faculty Research and Publications
Acoustic-to-articulatory inversion, the estimation of articulatory kinematics from an acoustic waveform, is a challenging but important problem. Accurate estimation of articulatory movements has the potential for significant impact on our understanding of speech production, on our capacity to assess and treat pathologies in a clinical setting, and on speech technologies such as computer aided pronunciation assessment and audio-video synthesis. However, because of the complex and speaker-specific relationship between articulation and acoustics, existing approaches for inversion do not generalize well across speakers. As acquiring speaker-specific kinematic data for training is not feasible in many practical applications, this remains an important and …
Vowel Production In Mandarin Accented English And American English: Kinematic And Acoustic Data From The Marquette University Mandarin Accented English Corpus, An Ji, Jeffrey J. Berry, Michael T. Johnson
Vowel Production In Mandarin Accented English And American English: Kinematic And Acoustic Data From The Marquette University Mandarin Accented English Corpus, An Ji, Jeffrey J. Berry, Michael T. Johnson
Speech Pathology and Audiology Faculty Research and Publications
Few electromagnetic articulography (EMA) datasets are publicly available, and none have focused systematically on non-native accented speech. We introduce a kinematic-acoustic database of speech from 40 (gender and dialect balanced) participants producing upper-Midwestern American English (AE) L1 or Mandarin Accented English (MAE) L2 (Beijing or Shanghai dialect base). The Marquette University EMA-MAE corpus will be released publicly to help advance research in areas such as pronunciation modeling, acoustic-articulatory inversion, L1-L2 comparisons, pronunciation error detection, and accent modification training. EMA data were collected at a 400 Hz sampling rate with synchronous audio using the NDI Wave System. Articulatory sensors were placed …