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2015 Vol.34, Issue 4 Preview Page
31 July 2015. pp. 310-315
Abstract
References
1
1.P. C. Loizou, Speech Enhancement (CRC Press, Boca Raton, 2007), pp. 309-400.
2
2.J. Sohn, N. S. Kim, and W. Sung, “A statistical model-based voice activity detection,” IEEE Signal Process. Lett. 16, 1–3 (1999).
3
3.ITU, A silence compression scheme for G.729 optimized for terminals conforming to recommendation V.70, ITU-T Recommendation G.729-Annex B (1996).
4
4.ETSI EN 301 708 V7.1.1(1999-12), Digital cellular tele-communications system(Phase 2+); VAD for AMR speech traffic channels; General Description (GSM 06.94 version 7.1.1 Release 1998), 13-14 (1999).
5
5.ETSI ES 202 050, Ver. 1.1.5(2007-01), Speech Processing, Transmission and Quality Aspects(STQ); Distributed Speech Recognition; Advanced front-end feature extraction algorithm; Compression algorithms, Annex A.3 Stage 2-VAD Logic, 42-43 (2007).
6
6.J. Ramirez, J. C. Segura, C. Benitez, A. Torre, and A. Rubio, “Efficient voice activity detection algorithms using long- term speech information,” Speech Commun. 42, 271-287 (2004).
7
7.A. Davis, S. Nordholm, and R. Togneri, “Statistical voice activity detection using low-variance spectrum estimation and an adaptive threshold,” IEEE Trans. Audio, Speech and Lang. Processing 14, 412-414 (2006).
8
8.G. Evangelopoulos and P. Maragos, “Multiband modulation energy tracking for noisy speech detection,” IEEE Trans. Audio, Speech and Lang. Processing 14, 2024-2038 (2006).
9
9.T. V. Pham and T. T. Chien, “Reliable voice activity detection algorithm under adverse environments,” in Proc. IEEE Int. Conf. Commun. Electronics, 218-223 (2008).
10
10.P. K. Ghosh and S. Narayanan, “Robust voice activity detection using long-term signal variability,” IEEE Trans. Audio, Speech and Lang. Processing 19, 600-613 (2011).
11
11.E. Chuangsuwanich and J. Glass, “Robust voice activity detector for real world application using harmonicity and modulation frequency,” in Proc. Interspeech, 2645-2648 (2011).
12
12.B. Koo, “A single channel voice activity detection for noisy environments using wavelet packet decomposition and Teager energy”  (in Korean), J. Acoust. Soc. Kr. 33, 139-145 (2014).
13
13. J. Garofolo, “TIMIT acoustic-phonetic continuous speech corpus,” LDC93S1, Linguistic Data Consortium, Philadelphia, 1993.
14
14. A. Varga and H. Steeneken, “Assessment for automatic speech recognition: II. NOISEX-92: An additive noise on speech recognition systems,” Speech Commun. 12, 247-251 (1993).
Information
  • Publisher :The Acoustical Society of Korea
  • Publisher(Ko) :한국음향학회
  • Journal Title :The Journal of the Acoustical Society of Korea
  • Journal Title(Ko) :한국음향학회지
  • Volume : 34
  • No :4
  • Pages :310-315
  • Received Date : 2015-01-29
  • Accepted Date : 2015-04-07