Paper Info
Title
Speaker Verification Using Adapted Articulatory Feature-based Conditional Pronunciation Modeling.
Abstract
This paper proposes a speaker verification system based on articulatory feature-based conditional pronunciation modeling (AFCPM). The system captures the pronunciation characteristics of speakers by modeling the linkage between the actual phones produced by the speakers and the state of articulations during speech production. The speaker models, which consist of condi- tional probabilities of two articulatory classes, are adapted from a set of universal background models (UBMs) via MAP adaptation. This creates a direct coupling between the speaker and background models, which prevents over-fitting the speaker models when the amount of speaker data is limited. Experimental results demon- strate that MAP adaptation not only enhances the discriminative power of the speaker models but also improves their robustness against handset mismatches. Results also show that fusing the scores derived from an AFCPM-based system and a conventional spectral-based system achieves an error rate that is significantly lower than that can be achieved by the individual systems. This suggests that AFCPM and spectral features are complementary to each other.
Year
DOI
Venue
2005
10.1109/ICASSP.2005.1415080
ICASSP (1)
Keywords
Field
DocType
signal processing,robustness,couplings,error rate,speech processing,maximum likelihood estimation,speaker recognition,probability,speech production,training data,conditional probabilities,natural languages
Pronunciation,Pattern recognition,Conditional probability,Computer science,Word error rate,Speech recognition,Robustness (computer science),Speaker recognition,Artificial intelligence,Speaker diarisation,Speech production,Discriminative model
Conference
Volume
ISSN
ISBN
1
1520-6149
0-7803-8874-7
Citations 
PageRank 
References 
1
0.37
8
Authors
4
Name
Order
Citations
PageRank
Ka-Yee Leung1142.85
Man-Wai Mak259469.36
Manhung Siu346461.40
Sun-Yuan Kung41853256.39