Detection of Voice Disorder Using Machine Learning

Detection of Voice Disorder Using Machine Learning 

Tejal Sawdekar¹, Pooja Kasar², Mansi More³  Anuja Rane⁴   

                                     ¹Tejal Sawdekar  Department of Information Technology  & Met Institute of Engineering

                       ²Pooja Kasar Department of Information Technology  & Met Institute of Engineering

                      ³Mansi More Department of Information Technology  & Met Institute of Engineering

                     ⁴ Anuja Rane Department  of Information Technology  & Met Institute of Engineering      

 ----------- ------------------------------------------------------------***------------------------------------------------------------------

Abstract - Voice disorder is a health issue that is frequently encountered, however, many patients either cannot afford to visit a professional doctor or neglect to take good care of their voice . In order to give a patient a preliminary diagnosis without using professional medical devices, previous research has shown that the detection of voice disorders can be carried out by utilizing machine learning and acoustic features extracted from voice recordings. Considering the increasing popularity of machine learning and feature learning, this study explores the possibilities of using these methods to assign voice recordings into one of the two classes Normal and Pathological . While the results show the general viability of machine learning and for the automatic recognition of voice disorder, they also demonstrate the shortcomings of the existing datasets for this task such as insufficient dataset size and lack of generality. The best accuracy in voice diseases detection is achieved by the(CNN). The proposed work uses spectrogram of voice recordings from a voice database as the input to a Convolutional Neural Network (CNN) for automatic feature extraction and classification of disordered and normal voice.

Key Words:  Voice recognition, Defective speech, Convolutional Neural Network, short time fourier transform, vocal fold.