Paper Info
Title
Unsupervised Data-Driven Hidden Markov Modeling For Text-Dependent Speaker Verification
Abstract
We present a text-dependent speaker verification system based on unsupervised data-driven Hidden Markov Models (HMMs) in order to take into account the temporal information of speech data. The originality of our proposal is to train unsupervised HMMs with only raw speech without transcriptions, that provide pseudo phonetic segmentation of speech data. The proposed text-dependent system is composed of the following steps. First, generic unsupervised HMMs are trained. Then the enrollment speech data for each target speaker is segmented with the generic models, and further processing is done in order to obtain speaker and text adapted HMMs, that will represent each speaker. During the test phase, in order to verify the claimed identity of the speaker, the test speech is segmented with the generic and the speaker dependent HMMs. Finally, two approaches based on log-likelihood ratio and concurrent scoring are proposed to compute the score between the test utterance and the speaker's model. The system is evaluated on Part1 of the RSR2015 database with Equal Error Rate (EER) on the development set, and Half Total Error Rate (HTER) on the evaluation set. An average EER of 1.29% is achieved on the development set, while for the evaluation part the average HTER is equal to 1.32%.
Year
DOI
Venue
2017
10.5220/0006202001990207
ICPRAM: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS
Keywords
Field
DocType
Unsupervised Data-driven Modeling, Hidden Markov Models, Text-dependent Speaker Verification, Concurrent Scoring
Speaker verification,Transcription (linguistics),Data-driven,Pattern recognition,Computer science,Segmentation,Markov model,Word error rate,Utterance,Artificial intelligence,Hidden Markov model
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Dijana Petrovska-Delacretaz1576.98
Houssemeddine Khemiri2274.14