Voice Disorder Detection Using Long Short Term Memory (LSTM) Model
This work addresses the problem of efficient screening for voice disorders in medical settings, but it is incremental as it applies an existing LSTM method to this domain.
The paper tackled automated detection of voice disorders by proposing an LSTM model, achieving 22% sensitivity, 97% specificity, and 56% unweighted average recall on a real dataset of 400 unlabeled testing samples.
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.