ITM Web Conf.
Volume 37, 2021International Conference on Innovative Technology for Sustainable Development (ICITSD-2021)
|Number of page(s)
|Innovative Technology for Sustainable Development
|17 March 2021
Speaker Recognition using Random Forest
School of Computer Sciences and Engineering, Vellore Institute of Technology, Chennai, India
* Corresponding author: email@example.com
Speaker identification has become a mainstream technology in the field of machine learning that involves determining the identity of a speaker from his/her speech sample. A person’s speech note contains many features that can be used to discriminate his/her identity. A model that can identify a speaker has wide applications such as biometric authentication, security, forensics and human-machine interaction. This paper implements a speaker identification system based on Random Forest as a classifier to identify the various speakers using MFCC and RPS as feature extraction techniques. The output obtained from the Random Forest classifier shows promising result. It is observed that the accuracy level is significantly higher in MFCC as compared to the RPS technique on the data taken from the well-known TIMIT corpus dataset.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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