Abstract
The paper presents a speaker detection system based on phoneme specific hidden Markov model in combination with Gaussian mixture model. Our motivation stems from the fact that the phoneme specific HMM system can model temporal variations and provides possibility to ponder the scores of specific phonemes as well as efficient pruning. The performance of the system has been evaluated on speech database which contains utterances in Serbian from 250 speakers (10 of them being the target speakers). The proposed model is compared to a system based on Gaussian mixture model - universal background model, and showed a significant improvement in detection performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Beigi, H.: Fundamentals of Speaker Recognition. Springer (2011)
Auckenthaler, R., Parris, E., Carey, M.: Improving a GMM speaker verification system by phonetic weighting. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 1999), vol. 1, pp. 313–316. Phoenix, Arizona (1999)
Kajarekar, S., Hermansky, H.: Speaker verification based on broad phonetic categories. In: A Speaker Odyssey - The Speaker Recognition Workshop (2001)
Hansen, E., Slyh, R., Anderson, T.: Speaker recognition using phoneme-specific GMMs. In: ODYSSEY 2004-The Speaker and Language Recognition Workshop, pp. 179–184 (2004)
Dunn, R., Reynolds, D., Quatieri, T.: Approaches to speaker detection and tracking in conversational speech. Digit. Signal Process. 10, 93–112 (2000)
Kinnunen, T., Li, H.: An Overview of Text-Independent Speaker Recognition: From Features to Supervectors. Speech Commun 52, 12–40 (2010)
Scheffer, N., Ferrer, L., Graciarena, M., Kajarekar, S., Shriberg, E., Stolcke, A.: The SRI NIST 2010 Speaker Recognition Evaluation System. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), pp. 5292–5295. Prague, Czech Republic (2011)
Antal, M.: Phonetic Speaker Recognition. In: 7th International Conference COMMUNICATIONS, pp. 67–72 (2008)
Reynolds, D., Quatieri, T., Dunn, R.: Speaker Verification Using Adapted Gaussian Mixture Models. Digit. Signal Process. 10, 19–41 (2000)
Delić, V., Sečujski, M., Jakovljević, N., Janev, M., Obradović, R., Pekar, D.: Speech Technologies for Serbian and Kindred South Slavic Languages. In: Advances in Speech Recognition, pp. 141–165 (2010)
Young, S.J., Evermann, G., Gales, M.J.F., Hain, T., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.C.: The HTK Book, version 3.4 (2006)
Gales, M., Young, S.: The Application of Hidden Markov Models in Speech Recognition. Foundations and Trends in Signal Processing 1(3), 195–304 (2007)
Jakovljević, N., Miškovic, D., Janev, M., Sečujski, M., Delić, V.: Comparison of Linear Discriminant Analysis Approaches in Automatic Speech Recognition. Elektronika Ir Elektrotechnika 19(7), 76–79 (2013)
Delić, V., Sečujski, M., Jakovljević, N., Pekar, D., Mišković, D., Popović, B., Ostrogonac, S., Bojanić, M., Knežević, D.: Speech and language resources within speech recognition and synthesis systems for serbian and kindred south slavic languages. In: Železný, M., Habernal, I., Ronzhin, A. (eds.) SPECOM 2013. LNCS, vol. 8113, pp. 319–326. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Pakoci, E., Jakovljević, N., Popović, B., Mišković, D., Pekar, D. (2014). Speaker Detection Using Phoneme Specific Hidden Markov Models. In: Ronzhin, A., Potapova, R., Delic, V. (eds) Speech and Computer. SPECOM 2014. Lecture Notes in Computer Science(), vol 8773. Springer, Cham. https://doi.org/10.1007/978-3-319-11581-8_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-11581-8_51
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11580-1
Online ISBN: 978-3-319-11581-8
eBook Packages: Computer ScienceComputer Science (R0)