26 citations · 36 across the 7 of their papers we have counts for
6 papers · 1 filter
On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches
Guilherme Schu, Parvaneh Janbakhshi, Ina Kodrasi
Although the UA-Speech and TORGO databases of control and dysarthric speech are invaluable resources made available to the research community with the objective of developing robus…
Experimental investigation on STFT phase representations for deep learning-based dysarthric speech detection
Parvaneh Janbakhshi, Ina Kodrasi
Mainstream deep learning-based dysarthric speech detection approaches typically rely on processing the magnitude spectrum of the short-time Fourier transform of input signals, whil…
Temporal envelope and fine structure cues for dysarthric speech detection using CNNs
Ina Kodrasi
Deep learning-based techniques for automatic dysarthric speech detection have recently attracted interest in the research community. State-of-the-art techniques typically learn neu…
Supervised Speech Representation Learning for Parkinson's Disease Classification
Parvaneh Janbakhshi, Ina Kodrasi
Recently proposed automatic pathological speech classification techniques use unsupervised auto-encoders to obtain a high-level abstract representation of speech. Since these repre…
Multi-task single channel speech enhancement using speech presence probability as a secondary task training target
L. Wang, J. Zhu, I. Kodrasi
To cope with reverberation and noise in single channel acoustic scenarios, typical supervised deep neural network~(DNN)-based techniques learn a mapping from reverberant and noisy…
Automatic dysarthric speech detection exploiting pairwise distance-based convolutional neural networks
P. Janbakhshi, I. Kodrasi, H. Bourlard
Automatic dysarthric speech detection can provide reliable and cost-effective computer-aided tools to assist the clinical diagnosis and management of dysarthria. In this paper we p…