CONF
SyncAlign-Esp99/IDIAP
CLIENT / WORLD MODEL SYNCHRONOUS ALIGNEMENT FOR SPEAKER VERIFICATION
Mariéthoz, Johnny
Genoud, Dominique
Bimbot, Frédéric
Mokbel, Chafic
EXTERNAL
http://publications.idiap.ch/attachments/papers/1999/SyncAlignEurospeech99.pdf
PUBLIC
http://publications.idiap.ch/index.php/publications/showcite/syncalignesp-irr99
Related documents
6th European Conference on Speech Communication and Technology --- Eurospeech'99
1999
Budapest, Hungary
IDIAP-RR 99-23
In speaker verification, two independent stochastic models, i.e. a client model and a non-client (world) model, are generally used to verify the claimed identity using a likelihood ratio score. This paper investigates a variant of this approach based on a common hidden process for both models. In this framework, both models share the same topology, which is conditioned by the underlying phonetic structure of the utterance. Then, two different output distributions are defined corresponding to the client vs. world hypotheses. Based on this idea, a synchronous decoding algorithm and the corresponding training algorithm are derived. Our first experiments on the SESP telephone database indicate a slight improvement with respect to a baseline system using independent alignments. Moreover, synchronous alignment offers a reduced complexity during the decoding process. Interesting perspectives can be expected. Keywords : Stochastic Modeling, HMM, Synchronous Alignment, EM algorithm
REPORT
SyncAlignEsp-IRR99/IDIAP
CLIENT / WORLD MODEL SYNCHRONOUS ALIGNEMENT FOR SPEAKER VERIFICATION
Mariéthoz, Johnny
Genoud, Dominique
Bimbot, Frédéric
Mokbel, Chafic
EXTERNAL
http://publications.idiap.ch/attachments/reports/1999/rr99-23.pdf
PUBLIC
Idiap-RR-23-1999
1999
IDIAP
In speaker verification, two independent stochastic models, i.e. a client model and a non-client (world) model, are generally used to verify the claimed identity using a likelihood ratio score. This paper investigates a variant of this approach based on a common hidden process for both models. In this framework, both models share the same topology, which is conditioned by the underlying phonetic structure of the utterance. Then, two different output distributions are defined corresponding to the client vs. world hypotheses. Based on this idea, a synchronous decoding algorithm and the corresponding training algorithm are derived. Our first experiments on the SESP telephone database indicate a slight improvement with respect to a baseline system using independent alignments. Moreover, synchronous alignment offers a reduced complexity during the decoding process. Interesting perspectives can be expected. Keywords : Stochastic Modeling, HMM, Synchronous Alignment, EM algorithm