  In this paper, we propose a classification based glottal closure instants
(GCI) detection from pathological acoustic speech signal, which finds many
applications in vocal disorder analysis. Till date, GCI for pathological
disorder is extracted from laryngeal (glottal source) signal recorded from
Electroglottograph, a dedicated device designed to measure the vocal folds
vibration around the larynx. We have created a pathological dataset which
consists of simultaneous recordings of glottal source and acoustic speech
signal of six different disorders from vocal disordered patients. The GCI
locations are manually annotated for disorder analysis and supervised learning.
We have proposed convolutional neural network based GCI detection method by
fusing deep acoustic speech and linear prediction residual features for robust
GCI detection. The experimental results showed that the proposed method is
significantly better than the state-of-the-art GCI detection methods.
