Evaluation of a Multi-Resolution Dyadic Wavelet Transform Method for usable Speech Detection
Many applications of speech communication and speaker
identification suffer from the problem of co-channel speech. This
paper deals with a multi-resolution dyadic wavelet transform method
for usable segments of co-channel speech detection that could be
processed by a speaker identification system. Evaluation of this
method is performed on TIMIT database referring to the Target to
Interferer Ratio measure. Co-channel speech is constructed by
mixing all possible gender speakers. Results do not show much
difference for different mixtures. For the overall mixtures 95.76% of
usable speech is correctly detected with false alarms of 29.65%.
[1] Morgan, P., George, E., Lee, T. and Kay, M., "Co-Channel Speaker
Separation", ICASSP, part 1, vol.1, pages: 828-831, May, 1995.
[2] Benincasa, D. S. and Savic, M. I., "Co-channel speaker separation using
constrained nonlinear optimization," Proc. IEEE ICASSP, pp: 1195-
1198, 1997.
[1] Meyer, G. F., Plante, F., and Bethommier, "Segregation of concurrent
speech with the reassignment spectrum," Proc. IEEE ICASSP, pp: 1203-
1206, 1997.
[2] Savic, M., Gao, H. and Sorensen, J. S., "Co-channel speaker separation
based on maximum-likelihood deconvolution,", IEEE ICASSP, pp:I-25-
I-28, 1994.
[3] Iyer, A. N., Smolenski, B. Y., Yantorno, R. E., Cupples, J., Wenndth, S.,
"Speaker Identification Improvement Using Usable Speech
Concept",EUSIPCO 2004
[4] S. Khanwalkar, Y. Smolenski, Robert E. Yantorno and S. J. Wenndt
"Enhancement of speaker identification using SID usable speech",
EUSIPCO 2005.
[5] Yantorno, R. E, "Method for improving speaker identification by
determining usable speech" J.ACOUST.SOC.AM. Volume 124, issue 5,
November 2008.
[6] R.Saeidi, P.Mowlaee, T.Kinnunen, Tan Zheng-Hua, M.G. Christensen,
S.H Jensen, P.Fränti, "Signal to signal ratio independent speaker
identification for co-channel speech signal" IEEE ICPR 2010.
[7] A. Kizhanatham and R. E. Yantorno, "Peak Difference Autocorrelation
of Wavelet Transform Algorithm Based Usable Speech Measure", 7th
World Multi-conference on Systemic, Cybernetics, and Informatics, Aug
2003
[8] J. M. Lovekin, K. R. Krishnamachari, and R. E. Yantorno, "Adjacent
pitch period comparison (appc) as a usability measure of speech
segments under co-channel conditions," IEEE International Symposium
on Intelligent Signal Processing and Communication Systems, pp. 139-
142, Nov 2001.
[9] N. Chandra and R. E. Yantorno, "Usable speech detection using
modified spectral autocorrelation peak to valley ration using the lpc
residual," 4th IASTED International Conference Signal and Image
Processing, pp. 146-150, 2002.
[10] N. Sundaram, A. N. Iyer, B. Y. Smolenski, and R. E. Yantorno, "Usable
speech detection using linear predictive analysis - a model-based
approach," IEEE International Symposium on Intelligent Signal
Processing and Communication Systems, ISPACS, 2003.
[11] A. N. Iyer, M. Gleiter, B. Y. Smolenski, and R. E. Yantorno, "Structural
usable speech measure using lpc residual," IEEE International
Symposium on Intelligent Signal Processing and Communication
Systems ,ISPACS, 2003.
[12] W.Ghezaiel, A.Ben Slimane, and E.Ben Braiek, "Usable speech
detection for speaker identification system under co-channel conditions",
JTEA 2010 Tunisia.
[13] Gonzalez, N. and Docampo, D., "Application of Singularity Detection
with Wavelets for Pitch Estimation of Speech Signals", Proc. EUSIPCO,
pp: 1657-1660, 1994.
[14] Janer, L. "New Pitch Detection Algorithm Based on Wavelet
Transform", IEEE-Signal Processing, pp: 165-168, 1998.
[15] J.I. Agbinya "Discrete wavelet transforms techniques in speech
processing" IEEE TENCON Digital Signal processing Application.
[16] Mallat SA, "Theory for MuItiresolution Signal Decomposition: The
Wavelet Representation", IEEE Transactions on Pattern Analysis
Machine Intelligence. Vol. 31, pp 674-693, 1989.
[17] Shi-huang Chen and Jhing-fa Wang "A pyramid structured wavelet
algorithm for detecting pitch period of speech signal" 1998 international
computer symposium.
[1] Morgan, P., George, E., Lee, T. and Kay, M., "Co-Channel Speaker
Separation", ICASSP, part 1, vol.1, pages: 828-831, May, 1995.
[2] Benincasa, D. S. and Savic, M. I., "Co-channel speaker separation using
constrained nonlinear optimization," Proc. IEEE ICASSP, pp: 1195-
1198, 1997.
[1] Meyer, G. F., Plante, F., and Bethommier, "Segregation of concurrent
speech with the reassignment spectrum," Proc. IEEE ICASSP, pp: 1203-
1206, 1997.
[2] Savic, M., Gao, H. and Sorensen, J. S., "Co-channel speaker separation
based on maximum-likelihood deconvolution,", IEEE ICASSP, pp:I-25-
I-28, 1994.
[3] Iyer, A. N., Smolenski, B. Y., Yantorno, R. E., Cupples, J., Wenndth, S.,
"Speaker Identification Improvement Using Usable Speech
Concept",EUSIPCO 2004
[4] S. Khanwalkar, Y. Smolenski, Robert E. Yantorno and S. J. Wenndt
"Enhancement of speaker identification using SID usable speech",
EUSIPCO 2005.
[5] Yantorno, R. E, "Method for improving speaker identification by
determining usable speech" J.ACOUST.SOC.AM. Volume 124, issue 5,
November 2008.
[6] R.Saeidi, P.Mowlaee, T.Kinnunen, Tan Zheng-Hua, M.G. Christensen,
S.H Jensen, P.Fränti, "Signal to signal ratio independent speaker
identification for co-channel speech signal" IEEE ICPR 2010.
[7] A. Kizhanatham and R. E. Yantorno, "Peak Difference Autocorrelation
of Wavelet Transform Algorithm Based Usable Speech Measure", 7th
World Multi-conference on Systemic, Cybernetics, and Informatics, Aug
2003
[8] J. M. Lovekin, K. R. Krishnamachari, and R. E. Yantorno, "Adjacent
pitch period comparison (appc) as a usability measure of speech
segments under co-channel conditions," IEEE International Symposium
on Intelligent Signal Processing and Communication Systems, pp. 139-
142, Nov 2001.
[9] N. Chandra and R. E. Yantorno, "Usable speech detection using
modified spectral autocorrelation peak to valley ration using the lpc
residual," 4th IASTED International Conference Signal and Image
Processing, pp. 146-150, 2002.
[10] N. Sundaram, A. N. Iyer, B. Y. Smolenski, and R. E. Yantorno, "Usable
speech detection using linear predictive analysis - a model-based
approach," IEEE International Symposium on Intelligent Signal
Processing and Communication Systems, ISPACS, 2003.
[11] A. N. Iyer, M. Gleiter, B. Y. Smolenski, and R. E. Yantorno, "Structural
usable speech measure using lpc residual," IEEE International
Symposium on Intelligent Signal Processing and Communication
Systems ,ISPACS, 2003.
[12] W.Ghezaiel, A.Ben Slimane, and E.Ben Braiek, "Usable speech
detection for speaker identification system under co-channel conditions",
JTEA 2010 Tunisia.
[13] Gonzalez, N. and Docampo, D., "Application of Singularity Detection
with Wavelets for Pitch Estimation of Speech Signals", Proc. EUSIPCO,
pp: 1657-1660, 1994.
[14] Janer, L. "New Pitch Detection Algorithm Based on Wavelet
Transform", IEEE-Signal Processing, pp: 165-168, 1998.
[15] J.I. Agbinya "Discrete wavelet transforms techniques in speech
processing" IEEE TENCON Digital Signal processing Application.
[16] Mallat SA, "Theory for MuItiresolution Signal Decomposition: The
Wavelet Representation", IEEE Transactions on Pattern Analysis
Machine Intelligence. Vol. 31, pp 674-693, 1989.
[17] Shi-huang Chen and Jhing-fa Wang "A pyramid structured wavelet
algorithm for detecting pitch period of speech signal" 1998 international
computer symposium.
@article{"International Journal of Electrical, Electronic and Communication Sciences:59034", author = "Wajdi Ghezaiel and Amel Ben Slimane Rahmouni and Ezzedine Ben Braiek", title = "Evaluation of a Multi-Resolution Dyadic Wavelet Transform Method for usable Speech Detection", abstract = "Many applications of speech communication and speaker
identification suffer from the problem of co-channel speech. This
paper deals with a multi-resolution dyadic wavelet transform method
for usable segments of co-channel speech detection that could be
processed by a speaker identification system. Evaluation of this
method is performed on TIMIT database referring to the Target to
Interferer Ratio measure. Co-channel speech is constructed by
mixing all possible gender speakers. Results do not show much
difference for different mixtures. For the overall mixtures 95.76% of
usable speech is correctly detected with false alarms of 29.65%.", keywords = "Co-channel speech, usable speech, multi-resolutionanalysis, speaker identification", volume = "5", number = "7", pages = "855-5", }
