Content Classification With Electroglottograph

Abstract

Electroglottograph(EGG) is a physiological signal collected from the throat which reflects the vocal cord movement. EGG signals can be still collected from patients without speaking ability or from the extremely noisy environments. Additionally, the trends of the vocal cord movement will be distinctive for long enough Chinese sentences with different contents. Therefore, it is valuable and possible to carry out the research of applying only the EGG signals for content classification or recognition. In this paper, a content classification method with EGG was proposed, which consists of an EGG feature extraction module and a classification network based on LSTM(Long Short-Term Memory) units. The EGG feature extraction module was composed of three parts: the voiced segments extraction, the feature extraction and the F 0 smoothing. The classification network was made of a three-layer bidirectional LSTM encoder. This method achieved 91.12% accuracy on the validation set in 20-class content classification experiment, which provides the reference for further study in content classification and recognition with EGG signals.