Paper Info
Title
A study on Turkish text - Dependent speaker recognition.
Abstract
Speaker recognition is a pattern recognition task which has long been studied, but the accuracies are still far from the desired levels. The majority of the studies on speaker recognition demonstrates the results obtained from databases in which English voices are used. Since there are very few studies on Turkish speech, the performance of the known successful methods in Turkish voices are uncertain. Therefore, in this study, the performance on the Turkish text - dependent system is investigated by using Gaussian Mixture Model - Universal Background Model (GMM - UBM) method which is a well known method in speaker recognition systems. In the experimental studies, Turkish speaker recognition database consisting of 46 speakers (36 males and 10 females) is used. Equal error rate (EER) is used to measure system performance. The equal error rate for GMM - UBM method was found to be 5.73%. It has been observed in the experiments that the speaker verification performance of GMM - UBM classifier on Turkish database is encouraging.
Year
Venue
Keywords
2017
Signal Processing and Communications Applications Conference
speaker recognition,gaussian mixture model,universal background model,mel - frequency cepstral coefficients
Field
DocType
ISSN
Speaker verification,Turkish,Computer science,Speaker recognition,Speaker diarisation,Natural language processing,Artificial intelligence,Classifier (linguistics),Pattern recognition,Word error rate,Speech recognition,Hidden Markov model,Mixture model
Conference
2165-0608
Citations 
PageRank 
References 
0
0.34
6
Authors
2
Name
Order
Citations
PageRank
Havva Celiktas100.34
Cemal Hanilçi217111.23