Open AccessVoice Feature Extraction for Gender and Emotion Recognition
Author(s) -
Vani Nair,
Pooja Pillai,
A. Sankara Subramanian,
Sarah Khalife,
Madhu Nashipudimath
Publication year - 2021
Publication title -
international journal on recent and innovation trends in computing and communication
Language(s) - English
Resource type - Journals
ISSN - 2321-8169
DOI - 10.17762/ijritcc.v9i5.5463
Subject(s) - mel frequency cepstrum , computer science , feature extraction , speech recognition , classifier (uml) , support vector machine , principal component analysis , emotion classification , voice analysis , artificial intelligence , perception , pattern recognition (psychology) , speech processing , psychology , neuroscience
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.