Fusion of acoustic and tokenization features for speaker recognition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4274 LNAI, Issue , 2006, Pages 566-577

  Tong, Rong ^a,b   Ma, Bin ^a   Lee, Kong Aik ^a   You, Changhuai ^a   Zhu, Donglai ^a   Kinnunen, Tomi ^a   Sun, Hanwu ^a   Dong, Minghui ^a   Chng, Eng Siong ^b   Li, Haizhou ^a,b  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Cepstral feature; Gaussian mixture model; Phonotactic feature; Speaker recognition; Support vector machine; Tokenization, fusion

Indexed keywords

CEPSTRAL FEATURES; GAUSSIAN MIXTURE MODEL; PHONOTACTIC FEATURE; SPEAKER RECOGNITION; TOKENIZATION;

COMMUNICATION CHANNELS (INFORMATION THEORY); IMAGE RETRIEVAL; LINGUISTICS; OBJECT RECOGNITION; SUPPORT VECTOR MACHINES; TELEPHONE SYSTEMS;

SPEECH RECOGNITION;

EID: 77249156046     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11939993_59     Document Type: Conference Paper

References (17)

1. Reynolds, D. A. Quatieri, T. F. and Dunn, R. B.: Speaker Verification Using Adapted Gaussian Mixture Modeling, Digital Signal Processing, 10 (2000), pp. 19-41.
2. Campbell, W. M., Campbell, J. P., Reynolds, D. A., Singer, E. and Torres-Carrasquillo, P. A.: Support Vector Machines for Speaker and Language Recognition. Computer Speech and Language, 20 (2006), pp. 210-229.
3. Doddington, G.: Speaker Recognition based on Idiolectal Differences between Speakers. Proc. Eurospeech, 2001.
4. Zissman, M. A.: Comparison of Four Approaches to Automatic Language Identification of Telephone Speech. IEEE Trans. on Speech and Audio Processing, vol.4, no.1, 1996.
5. Torres-Carrasquillo, P. A., Reynolds, D. A. and Deller, Jr., J. R.: Language Identification using Gaussian Mixture Model Tokenization. Proc. ICASSP, 2002.
6. Ma, B., Zhu, D., Tong, R. and Li, H.: Speaker cluster based GMM tokenization for speaker recognition. To appear in Interspeech 2006.
7. Auckenthaler, R., Carey, M. and Lloyd-Thomas, H.: Score normalization for textindependent speaker verification systems. Digital Signal Processing, 10, no 1-3, 42-54, Jan 2000. (Pubitemid 30592165)
8. Kinnunen, T. H., Koh, C. W. E., Wang, L., Li, H. and Chng, E.S.: Temporal Discrete Cosine Transform: Towards Longer Term Temporal Features for Speaker Verification, accepted for presentation in 5th International Symposium on Chinese Spoken Language Processing, 2006.
9. Li, H. and Ma, B.: A Phonotactic Language Model for Spoken Language Identification", 43rd Annual Meeting of the Association for Computational Linguistics (ACL05), June 2005, Ann Arbor, USA.
10. http://www.nist.gov/speech/tests/spk/2006/sre-06-evalplan-v9.pdf
11. Campbell, W.M.: "Generalized linear discrininant sequence kernels for speaker recognition," in Proc. ICASSP, pp. 161-164, 2002
12. Collobert,R.andBengio,S.:SVMTorch:supportvectormachinesforlarge- scaleregressionproblems.JournalofMachineLearningResearch,l,143-160,2001. (Pubitemid33738779)
13. th International Congress on Acoustics, 3:61-64, 1995.
14. Language Identification Corpus of the Institute for Infocomm Research
15. Wang, H.-C.: MAT-a project to collect Mandarin speech data through networks in Taiwa. Int. J. Comput. Linguistics Chinese Language Process. 1 (2) (February 1997) 73-89.
16. http://cslu.cse.ogi.edu/corpora/corpCurrent.html
17. Campbell, W. M., Campbell, J. P., Reynolds, D. A., Jones, D, A. and Leek, T. R.: Phonetic speaker recognition with support vector machines. Proc NIPS, 2003