Abstract
This paper presents a new algorithm for noise reduction in noisy speech recognition in autocorrelation domain. The autocorrelation domain is an appropriate domain for speech feature extraction due to its pole preserving and noise separation features. Therefore, we have investigated this domain for robust speech recognition.
In our proposed algorithm we have tried to suppress the effect of noise before using this domain for feature extraction. This suppression is carried out by noise autocorrelation sequence estimation from the first few frames in each utterance and subtracting it from the autocorrelation sequence of noisy signal. We tested our method on the Aurora 2 noisy isolated-word task and found its performance superior to that of other autocorrelation-based methods applied to this task.
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© 2006 Springer-Verlag Berlin Heidelberg
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Farahani, G., Ahadi, S.M., Homayounpour, M.M. (2006). Robust Feature Extraction of Speech Via Noise Reduction in Autocorrelation Domain. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_62
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DOI: https://doi.org/10.1007/11848035_62
Publisher Name: Springer, Berlin, Heidelberg
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