Voice Feature Extraction for Gender and Emotion Recognition

Abstract

Voice recognition plays a key function in spoken communication that facilitates identifying the emotions of a person that reflects within the voice. Gender classification through speech is a popular Human Computer Interaction (HCI) method on account that determining gender through computer is hard. This led to the development of a model for "Voice feature extraction for Emotion and Gender Recognition". The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have specific vocal traits because of their acoustical and perceptual variations along with a variety of emotions which bring their own specific perceptions. In order to explore this area, feature extraction requires pre-processing of data, which is necessary for increasing the accuracy. The proposed model follows steps such as data extraction, pre-processing using Voice Activity Detector(VAD), feature extraction using Mel-Frequency Cepstral Coefficient(MFCC), feature reduction by Principal Component Analysis(PCA) and Support Vector Machine (SVM) classifier. The proposed combination of techniques produced better results which can be useful in healthcare sector, virtual assistants, security purposes and other fields related to Human Machine Interaction domain.

Research on Chorus Emotion Recognition and Intelligent Medical Application Based on Health Big Data

“…The integrated HBD is stored in the temporary database and metadata of the cloud-wide HBD resource layer and then stored in the cloud-wide HBD integration layer, which is applied to practical application software to complete the integration of cloud-wide HBD based on integrated learning so as to ensure that the cloud-wide HBD resources have higher value embodiment and manageability when integrated [ 15 , 16 ].…”