Abstract
The behavior of the least squares filter (LeSF) is analyzed for a class of non-stationary signals that are composed of multiple sinusoids whose frequencies, phases and the amplitudes may vary from block to block and which are embedded in white noise. Analytic expressions for the weights and the output of the LeSF are derived as a function of the block length and the signal SNR computed over the corresponding block. Recognizing that such a sinusoidal model is a valid approximation to the speech signals, we have used LeSF filter estimated on each block to enhance the speech signals embedded in white noise. Automatic speech recognition (ASR) experiments on a connected numbers task, OGI Numbers95[20] show that the proposed LeSF based features yield an increase in speech recognition performance in various non-stationary noise conditions when compared directly to the un-enhanced speech and noise robust JRASTA-PLP features.
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References
Satorius, E., Zeidler, J., Alexander, S.: Linear predictive digital filtering of narrowband processes in additive broad-band noise. Naval Ocean Systems Center, San Diego, CA, Tech. Rep. 331 (November 1978)
Anderson, C.M., Satorius, E.H., Zeidler, J.R.: Adaptive Enhancement of Finite Bandwidth Signals in White Gaussian Noise. IEEE Trans. on ASSPÂ ASSP-31(1) (February 1983)
Zeidler, J.R., Satorius, E.H., Chabries, D.M., Wexler, H.T.: Adaptive Enhancement of Multiple Sinusoids in Uncorrelated Noise. IEEE Trans. on ASSPÂ ASSP-26(3) (June 1978)
Hermansky, H., Morgan, N.: Rasta Processing of Speech. IEEE Trans. on SAP 2(4) (October 1994)
Sambur, M.R.: Adaptive noise canceling for Speech signals. IEEE Trans. on ASSPÂ ASSP-26(5) (October 1978)
Haykin, S.: Adaptive Filter Theory. Prentice-Hall Publishers, NJ (1993)
McAulay, R.J., Quatieri, T.F.: Speech Analysis/Synthesis Based on a Sinusoidal Representation. IEEE Trans. on ASSPÂ ASSP-34(4) (August 1986)
Widrow, B., et al.: Adaptive noise cancelling: Principles and applications. Proc. IEEE 65, 1692–1716 (December 1975)
Sondhi, M., Berkley, D.: Silencing echoes on the telephone network. Proc. of IEEE 68, 948–963 (August 1980)
Gersho, A.: Adaptive equalization of highly dispersive channels for data transmission. Bell Syst. Tech. J. 48, 55–70 (January 1969)
Satorius, E., Alexander, S.T.: Channel equalization using adaptive lattice algorithms. IEEE Trans. Commun. 27, 899–905 (June 1979)
Satorius, E., Pack, J.: Application of least squares lattice algorithms for adaptive equalization. IEEE Trans. on Commun. COM-29, 136–142 (February 1981)
Bershad, N., Feintuch, P., Reed, F., Fisher, B.: Tracking characteristics of the LMS adaptive line-enhancer -Response to a linear chirp signal in noise. IEEE Trans. on ASSP ASSP-28, 504–517 (October 1980)
Griffiths, L.J.: A simple adaptive algorithm for real time processsing in antenna arrays. Proc. of IEEE 57, 1696–1704 (October 1969)
Frost, O.L.: An algorithm for linearly constrained adaptive array processing. Proc. of IEEE 60, 926–935 (August 1972)
Marple, L.: Efficient least squares FIR system identification. IEEE Trans. on ASSP ASSP-29, 62–73 (February 1981)
Davis, S.B., Mermelstein, P.: Comparison of Parametric Representation for Monosyllabic Word Recognition in Continuously Spoken Sentences. IEEE Trans. on ASSPÂ ASSP-28(4) (August 1980)
Young, S., Odell, J., Ollason, D., Valtchev, V., Woodland, P.: The HTK Book. Cambridge University, Cambridge (1995)
Cole, R.A., Fanty, M., Lander, T.: Telephone speech corpus at CSLU. In: Proc. of ICSLP, Yokohama, Japan (1994)
Varga, A., Steeneken, H., Tomlinson, M., Jones, D.: The NOISEX-92 study on the effect of additive noise on automatic speech recognition. Technical report, DRA Speech Research Unit, Malvern, England (1992)
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Tyagi, V., Wellekens, C. (2006). Least Squares Filtering of Speech Signals for Robust ASR. In: Renals, S., Bengio, S. (eds) Machine Learning for Multimodal Interaction. MLMI 2005. Lecture Notes in Computer Science, vol 3869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11677482_23
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DOI: https://doi.org/10.1007/11677482_23
Publisher Name: Springer, Berlin, Heidelberg
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