Paper Info
Title
Improved I-vector-based Speaker Recognition for Utterances with Speaker Generated Non-speech sounds.
Abstract
Conversational speech not only contains several variants of neutral speech but is also prominently interlaced with several speaker generated non-speech sounds such as laughter and breath. A robust speaker recognition system should be capable of recognizing a speaker irrespective of these variations in his speech. An understanding of whether the speaker-specific information represented by these variations is similar or not helps build a good speaker recognition system. In this paper, speaker variations captured by neutral speech of a speaker is analyzed by considering speech-laugh (a variant of neutral speech) and laughter (non-speech) sounds of the speaker. We study an i-vector-based speaker recognition system trained only on neutral speech and evaluate its performance on speech-laugh and laughter. Further, we analyze the effect of including laughter sounds during training of an i-vector-basedspeaker recognition system. Our experimental results show that the inclusion of laughter sounds during training seem to provide complementary speaker-specific information which results in an overall improved performance of the speaker recognition system, especially on the utterances with speech-laugh segments.
Year
Venue
Field
2017
arXiv: Sound
I vector,Laughter,Speech sounds,Recognition system,Computer science,Speech recognition,Speaker recognition system,Speaker recognition,Speaker diarisation
DocType
Volume
Citations 
Journal
abs/1705.09289
0
PageRank 
References 
Authors
0.34
5
3
Name
Order
Citations
PageRank
Sri Harsha Dumpala1115.04
Ashish Panda245.31
Sunil Kumar Kopparapu34225.18