Efficient Feature Extraction and De-noising Method for Chinese Speech Signals Using GGM-Based ICA
In this paper we study the ICA feature extraction method for Chinese speech signals. The generalized Gaussian model (GGM) is introduced as the p.d.f. estimator in ICA since it can provide a general method for modeling non-Gaussian statistical structure of univariate distributions. It is demonstrated that the ICA features of Chinese speech are localized in both time and frequency domain and the resulting coefficients are statistically independent and sparse. The GGM-based ICA method is also used in extracting the basis vectors directly from the noisy observation, which is an efficient method for noise reduction when priori knowledge of source data is not acquirable. The de-nosing experiments show that the proposed method is more efficient than conventional methods in the environment of additive white Gaussian noise.
KeywordsDiscrete Cosine Transform Speech Signal Discrete Fourier Transform Sparse Code Shrinkage Function
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