Speaker Recognition is the collective name of problems given to identifying a person or a set of persons using his/her voice. Variation of speaker speaking styles due to different languages can make speaker recognition a difficult task. In this paper, the main aim was to develop a system and compare different efficient text-independent Bengali speaker recognition systems that can give good rates of accuracy (greater than 90%) with not more than 10 minutes of speech data available for each speaker and can easily produce results without long amounts of delay. The experiments were carried out using the SHRUTI Bengali speech database and validated using TED-EX database. We have also analyzed different features of a Bengali speaker using GMM-UBM framework, Joint Factor Analysis, i-vectors, CNN and RNN. Elaborate comparisons and classifications are carried out based on training durations and languages spoken by the speakers.
Cite as: Das, S., Das, P.K. (2018) Analysis and Comparison of Features for Text-Independent Bengali Speaker Recognition. Proc. 6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018), 274-278, doi: 10.21437/SLTU.2018-57
@inproceedings{das18_sltu,
author={Shubhadeep Das and Pradip K. Das},
title={{Analysis and Comparison of Features for Text-Independent Bengali Speaker Recognition}},
year=2018,
booktitle={Proc. 6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)},
pages={274--278},
doi={10.21437/SLTU.2018-57}
}