Acoustic model adaptation using in-domain background models for dysarthric speech recognition
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[1] Raymond D. Kent,et al. An acoustic study of the relationships among neurologic disease, dysarthria type, and severity of dysarthria. , 2011, Journal of speech, language, and hearing research : JSLHR.
[2] Thomas S. Huang,et al. Real-world acoustic event detection , 2010, Pattern Recognit. Lett..
[3] Thomas S. Huang,et al. Non-frontal view facial expression recognition based on ergodic hidden Markov model supervectors , 2010, 2010 IEEE International Conference on Multimedia and Expo.
[4] Mark Hasegawa-Johnson,et al. State-Transition Interpolation and MAP Adaptation for HMM-based Dysarthric Speech Recognition , 2010, SLPAT@NAACL.
[5] Thomas S. Huang,et al. Novel Gaussianized vector representation for improved natural scene categorization , 2010, Pattern Recognit. Lett..
[6] Gary Weismer,et al. Classification and taxonomy of motor speech disorders: what are the issues? , 2010 .
[7] Xiaodan Zhuang,et al. Efficient object localization with gaussianized vector representation , 2009, IMCE '09.
[8] Thomas S. Huang,et al. Emotion recognition from speech VIA boosted Gaussian mixture models , 2009, 2009 IEEE International Conference on Multimedia and Expo.
[9] Mark Hasegawa-Johnson,et al. Acoustic fall detection using Gaussian mixture models and GMM supervectors , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.
[10] Thomas S. Huang,et al. Face age estimation using patch-based hidden Markov model supervectors , 2008, 2008 19th International Conference on Pattern Recognition.
[11] Thomas S. Huang,et al. Dysarthric speech database for universal access research , 2008, INTERSPEECH.
[12] Daniel Povey,et al. Universal background model based speech recognition , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[13] Thomas S. Huang,et al. Intersession variability compensation for language detection , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[14] S. Fager. Duration and variability in dysarthric speakers with traumatic brain injury , 2008 .
[15] Raymond D. Kent,et al. Listener agreement for auditory-perceptual ratings of dysarthria. , 2007, Journal of speech, language, and hearing research : JSLHR.
[16] Frank Rudzicz,et al. Comparing speaker-dependent and speaker-adaptive acoustic models for recognizing dysarthric speech , 2007, Assets '07.
[17] P. Green,et al. Automatic speech recognition and training for severely dysarthric users of assistive technology: The STARDUST project , 2006, Clinical linguistics & phonetics.
[18] Gary Weismer,et al. Philosophy of research in motor speech disorders , 2006, Clinical linguistics & phonetics.
[19] Mark Hasegawa-Johnson,et al. Landmark-based speech recognition: report of the 2004 Johns Hopkins summer workshop , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..
[20] Coarticulation • Suprasegmentals,et al. Acoustic Phonetics , 2019, The SAGE Encyclopedia of Human Communication Sciences and Disorders.
[21] Elmar Nöth,et al. Improving Children's Speech Recognition by HMM Interpolation with an Adults' Speech Recognizer , 2003, DAGM-Symposium.
[22] Sheri Hunnicutt,et al. An investigation of different degrees of dysarthric speech as input to speaker-adaptive and speaker-dependent recognition systems , 2001 .
[23] Roland Kuhn,et al. Rapid speaker adaptation in eigenvoice space , 2000, IEEE Trans. Speech Audio Process..
[24] Bronagh Blaney, John Wilson. Acoustic variability in dysarthria and computer speech recognition , 2000 .
[25] Douglas A. Reynolds,et al. Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..
[26] Raymond D. Kent,et al. Acoustic studies of dysarthric speech: methods, progress, and potential. , 1999, Journal of communication disorders.
[27] Koichi Shinoda,et al. Structural MAP speaker adaptation using hierarchical priors , 1997, 1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings.
[28] H. Timothy Bunnell,et al. The Nemours database of dysarthric speech , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.
[29] David R. Beukelman,et al. Disorders of motor speech : assessment, treatment, and clinical characterization , 1996 .
[30] Vassilios Digalakis,et al. Speaker adaptation using constrained estimation of Gaussian mixtures , 1995, IEEE Trans. Speech Audio Process..
[31] Philip C. Woodland,et al. Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models , 1995, Comput. Speech Lang..
[32] Chin-Hui Lee,et al. MAP Estimation of Continuous Density HMM : Theory and Applications , 1992, HLT.
[33] Chin-Hui Lee,et al. Bayesian Learning of Gaussian Mixture Densities for Hidden Markov Models , 1991, HLT.
[34] Lawrence S. Meyers,et al. Computer recognition of the speech of adults with cerebral palsy and dysarthria , 1991 .
[35] Stephen Cox,et al. Some statistical issues in the comparison of speech recognition algorithms , 1989, International Conference on Acoustics, Speech, and Signal Processing,.
[36] M. Fried-Oken,et al. Voice recognition device as a computer interface for motor and speech impaired people. , 1985, Archives of physical medicine and rehabilitation.
[37] A. Aronson,et al. Motor Speech Disorders , 2014 .
[38] A. Aronson,et al. Clusters of deviant speech dimensions in the dysarthrias. , 1969, Journal of speech and hearing research.
[39] A. Aronson,et al. Differential diagnostic patterns of dysarthria. , 1969, Journal of speech and hearing research.
[40] L. Baum,et al. An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology , 1967 .
[41] H. Kucera,et al. Computational analysis of present-day American English , 1967 .
[42] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .