01773caa a2200205   4500008004100000022001400041100002500055245009100080260001500171270009400186270011100280270011100391300001400502490002300516520089000539700001601429700001801445773005701463856004701520210629s9999    xx            000 0 und d  a1941-0484  aChandrashekar, H. M. 0aSpectro-Temporal Representation of Speech for Intelligibility Assessment of Dysarthria  bIEEEc2020  aDepartment of Clinical Sciences, All India Institute of Speech and Hearing, Mysore, India  aDepartment of Electronics and Communication Engineering, Siddaganga Institute of Technology, Tumkur, India  aDepartment of Electronics and Communication Engineering, Siddaganga Institute of Technology, Tumkur, India  a390 - 399  vVolume 14  Issue 2  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.  aSreedevi, N  aVeena Karjigi  aIEEE Journal of Selected Topics in Signal Processing  uhttps://doi.org/10.1109/JSTSP.2019.2949912