Paper Info
Title
A Study on Text-Independent Speaker Recognition Systems in Emotional Conditions Using Different Pattern Recognition Models.
Abstract
The present study focuses on the text-independent speaker recognition in emotional conditions. In this paper, both system and source features are considered to represent speaker specific information. At the model level, Gaussian Mixture Models (GMMs), Gaussian Mixture Model-Universal Background Model (GMM-UBM) and Deep Neural Networks (DNN) are explored. The experiments are performed using 3 emotional databases, i.e. German emotional speech database (EMO-DB), IITKGP-SESC: Hindi and IITKGP-SESC: Telugu databases. The emotions considered in the present study are neutral, anger, happy and sad. The results show that, the performance of a speaker recognition system trained with clean speech is degrading while testing with emotional data irrespective of feature used or model used to build the system. The best results are obtained for the score level fusion of system and source features based systems when speakers are modeled with DNNs.
Year
DOI
Venue
2016
10.1007/978-3-319-58130-9_7
Lecture Notes in Artificial Intelligence
Keywords
Field
DocType
Speaker recognition,Emotion,System features,Source features,Gaussian Mixture Modeling,Universal Background Modeling,Deep Neural Networks
Computer science,Hindi,Speech recognition,Speaker recognition,Gaussian,Specific-information,Anger,Deep neural networks,Mixture model,Telugu
Conference
Volume
ISSN
Citations 
10089
0302-9743
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
K. N. R. K. Raju Alluri172.20
Sivanand Achanta2133.69
Rajendra Prasath32310.11
Gangashetty, S.V.4205.71
Anil Kumar Vuppala5275.71