Voice Feature Extraction for Gender and Emotion Recognition

Abstract

Voice recognition plays a key role in spoken communication that helps to identify the emotions of a person that reflects in the voice. Gender classification through speech is a widely used Human Computer Interaction (HCI) as it is not easy to identify gender by computer. 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 different voice characteristics due to their acoustical and perceptual differences along with a variety of emotions which convey their own unique 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 the healthcare sector, virtual assistants, security purposes and other fields related to the Human Machine Interaction domain.

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

“…Prosodic features and phonetic features are not isolated. Literature [ 23 , 24 ] show that there is a certain correlation between prosodic features of speech signals and three emotional dimensions (valence dimension, activation dimension, and control dimension). Among them, the activation dimension is obviously related to prosodic features, and emotional states with similar activation dimensions have similar prosodic features, which are easy to be confused.…”