Deep Learning for Speaker Recognition

Automated speaker recognition has become increasingly popular to aid in crime investigations and authorization processes with the advances in computer science. Speaker recognition or broadly speech recognition has been an active area of research for the past two decades. There has been significant improvement in the recognition accuracy due to the recent resurgence of deep neural networks. In this work we built a LSTM based speaker recognition system on a dataset collected from Cousera lectures. We achieved an accuracy of 93%. Prior to applying deeplearning techniques, we tested on a base-line using feed-forward network on a different dataset and achieved an accuracy of 96.48%. Results show that the deep learning network could detect the speakers very well except in cases where there is significant overlap in the speaker’s accent and tone.