Acoustic Assessment of Disordered Voice with Continuous Speech Based on Utterance-Level ASR Posterior Features
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[1] Haizhou Li,et al. Combining multiple kernel models for automatic intelligibility detection of pathological speech , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[2] Tan Lee,et al. Spoken language resources for Cantonese speech processing , 2002, Speech Commun..
[3] M. Brewer,et al. Research Design and Issues of Validity , 2000 .
[4] Brigitte Bigi,et al. Automatic word segmentation for spoken Cantonese , 2015, 2015 International Conference Oriental COCOSDA held jointly with 2015 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE).
[5] Daniel Povey,et al. The Kaldi Speech Recognition Toolkit , 2011 .
[6] Rahul Gupta,et al. Pathological speech processing: State-of-the-art, current challenges, and future directions , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[7] Yuanyuan Liu,et al. Predicting Severity of Voice Disorder from DNN-HMM Acoustic Posteriors , 2016, INTERSPEECH.
[8] Thomas Law,et al. Comparison of Rater's reliability on perceptual evaluation of different types of voice sample. , 2012, Journal of voice : official journal of the Voice Foundation.
[9] Rahul Gupta,et al. Automatic estimation of parkinson's disease severity from diverse speech tasks , 2015, INTERSPEECH.
[10] Definitions of communication disorders and variations. Ad Hoc Committee on Service Delivery in the Schools. American Speech-Language-Hearing Association. , 1993, ASHA. Supplement.
[11] Helen Meng,et al. CANTONESE SPEECH RECOGNITION AND SYNTHESIS , 2006 .
[12] Naveen Kumar,et al. Automatic intelligibility classification of sentence-level pathological speech , 2015, Comput. Speech Lang..