NDLI: Assessment of dysarthric speech using Elman back propagation network (recurrent network) for speech recognition

Content Provider	Springer Nature Link
Author	Selva Nidhyananthan, S. Shantha Selva kumari, R. Shenbagalakshmi, V.
Copyright Year	2016
Abstract	In this paper, the role of speech recognition system in the assessment of dysarthric speech based on a method called Elman back propagation network (EBN) is studied. Dysarthria is a neurological disability that damages the control of motor speech articulators. The persons who suffer from Dysarthria may have speech intelligibility rate which may vary from low (2 %) to high (95 %). EBN is a Recurrent network, here a fully connected neural network is built such that the speech characteristics are represented simultaneously by neuron activation states. It is an efficient self supervised training algorithm. For parametric representation of the speech signal, we used Glottal feature along with mel frequency cepstral coefficients. Then finally the output of both the features is compared after the evaluation process using different neural networks and modeling methods. Evaluation of the proposed method is done on the subset of the Universal Access Research database. The subset consists of 9 dysarthric speakers out of 19 speakers each uttering 100 words repeatedly 3 times. The promising performance of the proposed system can be successfully applied to help the people who work for the voice disorder persons.
Starting Page	577
Ending Page	583
Page Count	7
File Format	PDF
ISSN	13812416
Journal	International Journal of Speech Technology
Volume Number	19
Issue Number	3
e-ISSN	15728110
Language	English
Publisher	Springer US
Publisher Date	2016-07-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neurological disability Speech intelligibility EBN Glottal features UA Dysarthria Signal, Image and Speech Processing Social Sciences Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Human-Computer Interaction Computer Vision and Pattern Recognition Software Linguistics and Language

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