Speaker Identification Accuracy Improvement Using BLSTM Neural Network

Abstract

In this work we analyze speaker identification accuracy on Lithuanian speaker dataset LIEPA. This dataset consists of 370 Lithuanian speakers reading given text samples. We preform speaker identification with HMM classification and then repeat the same test with different types of LSTM and BLSTM neural networks. On the given dataset we experimentally observe speaker identification accuracy improvement from 3% to 6% compared to best HMM implementation.