An Approach for Identification of Speaker using Deep Learning

Abstract

The audio data is getting increased on daily basis across the world with the increase of telephonic conversations, video conferences, podcasts and voice notes. This study presents a mechanism for identification of a speaker in an audio file, which is based on the biometric features of human voice such as frequency, amplitude, and pitch. We proposed an unsupervised learning model which uses wav2vec 2.0 where the model learns speech representation with the dataset provided. We used Librispeech dataset in our research and we achieved our results at an error rate which is 1.8.