Proceedings ArticleDOI
HMM-based speech recognition system for the dysarthric speech evaluation of articulatory subsystem
S. Lilly Christina,P. Vijayalakshmi,T. Nagarajan +2 more
- pp 54-59
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TLDR
In this work, isolated-style, phoneme recognition system is developed using monophone as the sub word unit and correlates well with the Frenchey dysarthria assessment (FDA) scores provided with the Nemours database.Abstract:
Dysarthria is a neuromotor impairment of speech that affects one or more of the speech subsystems, but is often associated with irregular co-ordination of articulators and restricted movement of articulators among other problems. It is reflected in the acoustic characteristics of the phonemes as deviations from their healthy counterparts. To capture these deviations, in this work, isolated-style, phoneme recognition system is developed using monophone as the sub word unit. The performance of this phoneme recognition system for a dysarthric speaker can be directly related to the severity of the problem. To train the sub word unit models, speech data is collected from seven normal speakers. Time-aligned phonetic transcriptions are derived using forced Viterbi alignment procedure. Using this data, hidden Markov models for the required phonemes are trained. Nemours database contains time-aligned phonetic transcriptions for all the ten dysarthric speakers. Using these transcriptions, phonetic inventory is created for each of the dysarthric speakers separately. These phoneme segments are tested with the phoneme models trained using the normal speakers' data. The performance of this speech recognition system is analyzed after phoneme grouping, based on the place of articulation, for the assessment of the articulatory subsystem of the dysarthric speech. The analysis output correlates well with the Frenchey dysarthria assessment (FDA) scores provided with the database.read more
Citations
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Proceedings ArticleDOI
Development of the CUHK Dysarthric Speech Recognition System for the UA Speech Corpus.
Jianwei Yu,Xurong Xie,Shansong Liu,Shoukang Hu,Max W. Y. Lam,Xixin Wu,Ka-Ho Wong,Xunying Liu,Helen Meng +8 more
TL;DR: This paper presents the development of the Chinese University of Hong Kong automatic speech recognition (ASR) system for the Universal Access Speech (UASpeech) and a range of deep neural network (DNN) acoustic models and their more advanced variants based on time delayed neural networks (TDNNs) and long short-term memory recurrent Neural networks (LSTM-RNNs).
Journal ArticleDOI
Evolutionary approach for integration of multiple pronunciation patterns for enhancement of dysarthric speech recognition
TL;DR: This paper presents an approach that integrates multiple pronunciation patterns for enhancement of dysarthric speech recognition by weighting the responses of an Automatic Speech Recognition (ASR) system when different language model restrictions are set.
Automatic speech processing for dysarthria: A study of Inter-pathology variability
TL;DR: The observations of the segmentation outputs yielded by the automatic tool according to the pathologies, the type of dysarthria and different phonetic categories reveal a very large heterogeneity of behavior between pathologies and within the same pathology.
Patent
Assessment of a pulmonary condition by speech analysis
Chaim Lotan,Sigal Kremer-Tal,Aviv Lotan,Zeev Schlik,Avinoam Gemer,Yehuda Snir,Yonatan Sasson,Margarita Sheinkerman +7 more
TL;DR: In this paper, the authors describe an apparatus that includes a network interface and a processor, which is configured to receive, via the network interface, speech of a subject who suffers from a pulmonary condition related to accumulation of excess fluid.
Proceedings ArticleDOI
Significance of Feature Selection for Acoustic Modeling in Dysarthric Speech Recognition
TL;DR: A comparative study of various feature extraction methods on dysarthric speech shows that MFCC and PLP gave better results than filter bank and reflection coefficients for dysarthic speech analysis.
References
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Proceedings ArticleDOI
The Nemours database of dysarthric speech
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Proceedings ArticleDOI
Hmm-Based and Svm-Based Recognition of the Speech of Talkers With Spastic Dysarthria
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Proceedings ArticleDOI
Towards the improvement of automatic recognition of dysarthric speech
TL;DR: A procedure in which formants and energies are estimated from dysarthic speech and modified to more closely approximately desired normal targets is used, which raises the recognition rate of the dysarthric speech from 28% to 71.4%.
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