Identifying Lithuanian native speakers using voice recognition

Abstract

In this paper, we analyze speaker identification and present identification test results on Lithuanian native speakers’ database LIEPA. Two approaches for speaker acoustic modeling are examined. We start by extracting MFCC features from audio samples, then we feed this data to create speaker acoustic model with hidden Markov models (1) and with deep neural networks (2). We compare both methods by nalyzing the subset of samples from LIEPA database. This helps to achieve more than 96% identification accuracy on sample dataset.