Speaker Recognition using Random Forest
نویسندگان
چکیده
Speaker identification has become a mainstream technology in the field of machine learning that involves determining identity speaker from his/her speech sample. A person’s note contains many features can be used to discriminate identity. model identify wide applications such as biometric authentication, security, forensics and human-machine interaction. This paper implements system based on Random Forest classifier various speakers using MFCC RPS feature extraction techniques. The output obtained shows promising result. It is observed accuracy level significantly higher compared technique data taken well-known TIMIT corpus dataset.
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ژورنال
عنوان ژورنال: ITM web of conferences
سال: 2021
ISSN: ['2271-2097', '2431-7578']
DOI: https://doi.org/10.1051/itmconf/20213701022