000			01829caa a2200229 4500
008			210629s9999 xx 000 0 und d
022			_a1941-0484
100			_aChandrashekar, H. M.
245		0	_aSpectro-Temporal Representation of Speech for Intelligibility Assessment of Dysarthria
260			_bIEEE _c2020
270			_aDepartment of Clinical Sciences, All India Institute of Speech and Hearing, Mysore, India
270			_aDepartment of Electronics and Communication Engineering, Siddaganga Institute of Technology, Tumkur, India
270			_aDepartment of Electronics and Communication Engineering, Siddaganga Institute of Technology, Tumkur, India
300			_a390 - 399
490			_vVolume 14 Issue 2
520			_aRecently, spectro-temporal representation of speech has been used in many fields of speech processing. Owing to this, we explore the use of spectro-temporal representation for speech intelligibility assessment especially for dysarthric speech. In this work, we investigate the use of spectro-temporal representations to evaluate intelligibility levels using artificial neural network (ANN) and convolutional neural network (CNN). Standard American English dysarthric databases namely Universal Access and TORGO are used for evaluation. Performance of CNN classifier is superior to ANN as it is an advanced classifier. Further, use of Time-Frequency CNN configuration proved to capture spectro-temporal variations together resulting in an improved performance compared to either Time-CNN or Frequency-CNN configurations which capture either temporal or spectral variations respectively.
700			_aSreedevi, N
700			_aVeena Karjigi
773			_aIEEE Journal of Selected Topics in Signal Processing
856			_uhttps://doi.org/10.1109/JSTSP.2019.2949912
942			_cFJAS _yFJAS
999			_c17797 _d17797