  Automated detection of voice disorders with computational methods is a recent
research area in the medical domain since it requires a rigorous endoscopy for
the accurate diagnosis. Efficient screening methods are required for the
diagnosis of voice disorders so as to provide timely medical facilities in
minimal resources. Detecting Voice disorder using computational methods is a
challenging problem since audio data is continuous due to which extracting
relevant features and applying machine learning is hard and unreliable. This
paper proposes a Long short term memory model (LSTM) to detect pathological
voice disorders and evaluates its performance in a real 400 testing samples
without any labels. Different feature extraction methods are used to provide
the best set of features before applying LSTM model for classification. The
paper describes the approach and experiments that show promising results with
22% sensitivity, 97% specificity and 56% unweighted average recall.
