Abstract
In the paper we demonstrate a complex supervised learning method based on a binary decision graphs. This method is employed in construction of a silence/speech detector. Performance of the resulting silence/speech detector is compared with performance of common silence/speech detectors used in telecommunications and with a detector based on HMM and a bigram silence/speech language model. Each non-leaf node of a decision graph has assigned a question and a sub-classifier answering this question. We test three kinds of these sub-classifiers: linear classifier, classifier based on separating quadratic hyper-plane (SQHP), and Support Vector Machines (SVM) based classifier. Moreover, besides usage of a single decision graph we investigate application of a set of binary decision graphs.
Support for this work was provided by the Grant Agency of Academy of Sciences of the Czech Republic, project No. 1ET101470416 and by the Ministry of Education of the Czech Republic, project No. MŠMT LC536.
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
Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, New York (1999)
Platt, J.: Using Sparseness and Analytic QP to Speed Training of Support Vector Machines. In: Kearns, M.S., Solla, S.A., Cohn, D.A. (eds.) Advances in Neural Information Processing Systems 11. MIT Press, Cambridge (1999)
Voice Activity Detector for Adaptive Multi-Rate speech traffic channels, GSM 06.94 version 7.1.1 Release 1994 Telecommunications Standards Institute (1994)
AMR Wideband speech codec; Voice Activity Detector (VAD), 3GPP TS 26.194 version 6.0.0 Release 6. European Telecommunications Standards Institute (1994)
VAD for Coding of Speech at 8 kbit/s Using Conjugate-Structure Algebraic-Code-Excited Linear-Prediction (CS-ACELP) – ITU-T Recommendation G.729 Annex B
Müller, L., Psutka, J.: Building robust PLP-based acoustic module for ASR applications. In: SPECOM 2005 proceedings, Moscow State Linguistic University, Moscow, pp. 761–764 (2005), ISBN 5-7452-0110-X
Radová, V., Psutka, J.: UWB_S01 Corpus: A Czech Read-Speech Corpus. In: Proceedings of the 6th International Conference on Spoken Language Processing ICSLP 2000, Beijing 2000, China. vol. IV, pp. 732–735 (2000)
Chu, W.C.: Speech Coding Algorithms: Foundation and Evolution of Standardized Coders. John Wiley and Sons, New Jersey (2003)
Šmídl, L., Prcín, M., Jurčíček, F.: How to Detect Speech in Telephone Dialogue Systems. In: Proceedings of EURASIP Conference on Digital Signal Processing for Multimedia Communications and Services ECMCS, Hungary, Budapest (CD-ROM) (2001), ISBN 963-8111-64-X
Cornu, E., Sheikhzadeh, H., Brennan, R.L., Abutalebi, H.R., et al.: ETSI AMR-2 VAD: Evaluation and Ultra Low-Resource Implementation. In: International Conference on Acoustics Speech and Signal Processing (ICASSP 2003) (2003), Available at: www.amis.com/tech_resources/dsp_technology_papers/ICASSP2003_VAD.pdf
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Trmal, J., Zelinka, J., Vaněk, J., Müller, L. (2006). Silence/Speech Detection Method Based on Set of Decision Graphs. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2006. Lecture Notes in Computer Science(), vol 4188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11846406_68
Download citation
DOI: https://doi.org/10.1007/11846406_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39090-9
Online ISBN: 978-3-540-39091-6
eBook Packages: Computer ScienceComputer Science (R0)