Bulbar ALS Detection Based on Analysis of Voice Perturbation and Vibrato
暂无分享,去创建一个
Maxim Vashkevich | Alexander Petrovsky | Yuliya Rushkevich | A. Petrovsky | Maxim Vashkevich | Y. Rushkevich
[1] Peter A. Flach,et al. Machine Learning - The Art and Science of Algorithms that Make Sense of Data , 2012 .
[2] F. Piehl,et al. Risk factors for amyotrophic lateral sclerosis , 2015, Clinical epidemiology.
[3] Arnold E. Aronson,et al. Rapid Voice Tremor, or “Flutter,” in Amyotrophic Lateral Sclerosis , 1992, The Annals of otology, rhinology, and laryngology.
[4] Myung Jong Kim,et al. Automatic Early Detection of Amyotrophic Lateral Sclerosis from Intelligible Speech Using Convolutional Neural Networks , 2018, INTERSPEECH.
[5] E. Castillo Guerra,et al. A modern approach to dysarthria classification , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).
[6] Masataka Goto,et al. An automatic singing skill evaluation method for unknown melodies using pitch interval accuracy and vibrato features , 2006, INTERSPEECH.
[7] I. Titze,et al. Comparison of Fo extraction methods for high-precision voice perturbation measurements. , 1993, Journal of speech and hearing research.
[8] J. Green,et al. DETECTION OF BULBAR ALS USING A COMPREHENSIVE SPEECH ASSESSMENT BATTERY , 2013 .
[9] Carla Agurto,et al. Detection of Amyotrophic Lateral Sclerosis (ALS) via Acoustic Analysis , 2018, bioRxiv.
[10] Prasanta Kumar Ghosh,et al. Comparison of Speech Tasks for Automatic Classification of Patients with Amyotrophic Lateral Sclerosis and Healthy Subjects , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[11] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[12] J M Kates,et al. Room reverberation effects in hearing aid feedback cancellation. , 2001, The Journal of the Acoustical Society of America.
[13] Ahmed Hammouch,et al. Discriminating Between Patients With Parkinson’s and Neurological Diseases Using Cepstral Analysis , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[14] Robert F. Kubichek,et al. Design of a dysarthria classifier using global statistics of speech features , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.
[15] J R Orozco-Arroyave,et al. Automatic detection of Parkinson's disease in running speech spoken in three different languages. , 2016, The Journal of the Acoustical Society of America.
[16] Yana Yunusova,et al. Classification of Bulbar ALS from Kinematic Features of the Jaw and Lips: Towards Computer-Mediated Assessment , 2017, INTERSPEECH.
[17] Elias Azarov,et al. Instantaneous pitch estimation based on RAPT framework , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).
[18] Thomas F. Quatieri,et al. Relation of Automatically Extracted Formant Trajectories with Intelligibility Loss and Speaking Rate Decline in Amyotrophic Lateral Sclerosis , 2016, INTERSPEECH.
[19] Ronald J. Baken,et al. Clinical measurement of speech and voice , 1987 .
[20] Paul Boersma,et al. Praat, a system for doing phonetics by computer , 2002 .
[21] Ashok Samal,et al. Fractal features for automatic detection of dysarthria , 2017, 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).
[22] Elias Azarov,et al. Features extraction for the automatic detection of ALS disease from acoustic speech signals , 2018, 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA).
[23] Paul Boersma,et al. Should jitter be measured by peak picking or by waveform matching , 2009 .