暂无分享,去创建一个
[1] Frank Rudzicz,et al. The TORGO database of acoustic and articulatory speech from speakers with dysarthria , 2011, Language Resources and Evaluation.
[2] Elmar Nöth,et al. Characterisation of voice quality of Parkinson's disease using differential phonological posterior features , 2017, Comput. Speech Lang..
[3] Stephen J. Cox,et al. Modelling Errors in Automatic Speech Recognition for Dysarthric Speakers , 2009, EURASIP J. Adv. Signal Process..
[4] James J. Jiang. A Literature Survey on Domain Adaptation of Statistical Classifiers , 2007 .
[5] K. Hux,et al. Speech recognition training for enhancing written language generation by a traumatic brain injury survivor. , 2000, Brain injury.
[6] Massimiliano Pontil,et al. Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport , 2020, ArXiv.
[7] Yifan Gong,et al. Low-rank plus diagonal adaptation for deep neural networks , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[8] Yifan Gong,et al. Adversarial Speaker Adaptation , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[9] Elmar Nöth,et al. Convolutional Neural Network to Model Articulation Impairments in Patients with Parkinson's Disease , 2017, INTERSPEECH.
[10] Yossi Matias,et al. Personalizing ASR for Dysarthric and Accented Speech with Limited Data , 2019, INTERSPEECH.
[11] Horacio Franco,et al. Articulatory Features for ASR of Pathological Speech , 2018, INTERSPEECH.
[12] Yanning Zhang,et al. An unsupervised deep domain adaptation approach for robust speech recognition , 2017, Neurocomputing.
[13] L. Kantorovich. On the Translocation of Masses , 2006 .
[14] I-Fan Chen,et al. Maximum a posteriori adaptation of network parameters in deep models , 2015, INTERSPEECH.
[15] Wouter M. Kouw,et al. A review of single-source unsupervised domain adaptation , 2019, ArXiv.
[16] Klaus-Robert Müller,et al. Covariate Shift Adaptation by Importance Weighted Cross Validation , 2007, J. Mach. Learn. Res..
[17] Nancy Thomas-Stonell,et al. Effects of speech training on the accuracy of speech recognition for an individual with a speech impairment , 1997 .
[18] Colin Raffel,et al. librosa: Audio and Music Signal Analysis in Python , 2015, SciPy.
[19] Ciro Martins,et al. Speaker-adaptation for hybrid HMM-ANN continuous speech recognition system , 1995, EUROSPEECH.
[20] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[21] Guozhen An,et al. Automatic recognition of unified parkinson's disease rating from speech with acoustic, i-vector and phonotactic features , 2015, INTERSPEECH.
[22] Phil D. Green,et al. Automatic speech recognition with sparse training data for dysarthric speakers , 2003, INTERSPEECH.
[23] Yves Normandin,et al. Noise adaptation algorithms for robust speech recognition , 1993, Speech Commun..
[24] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[25] Satrajit S. Ghosh,et al. Segment-dependent dynamics in predicting parkinson's disease , 2015, INTERSPEECH.
[26] Visar Berisha,et al. Interpretable Objective Assessment of Dysarthric Speech Based on Deep Neural Networks , 2017, INTERSPEECH.
[27] Pietro Laface,et al. Linear hidden transformations for adaptation of hybrid ANN/HMM models , 2007, Speech Commun..
[28] Chng Eng Siong,et al. Severity-Based Adaptation with Limited Data for ASR to Aid Dysarthric Speakers , 2014, PloS one.
[29] Sheri Hunnicutt,et al. An investigation of different degrees of dysarthric speech as input to speaker-adaptive and speaker-dependent recognition systems , 2001 .
[30] Nicolas Courty,et al. Joint distribution optimal transportation for domain adaptation , 2017, NIPS.
[31] Dirk Van Compernolle. Noise adaptation in a hidden Markov model speech recognition system , 1989 .