@InProceedings{messaoud2017_219,
author = {Mohamed anouar Ben messaoud and Aïcha Bouzid},
booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2017},
title = {An Improved Thresholding Function and Sparse Subspace decomposition for Speech Enhancement and its Application to Speech Recognition},
year = {2017},
editor = {Jürgen Trouvain and Ingmar Steiner and Bernd Möbius},
month = mar,
pages = {50--57},
publisher = {TUDpress, Dresden},
abstract = {In this work, we propose an unsupervised monaural Arabic speech
enhancement method that is based on two different techniques. The main idea is to
determine an exact threshold value in the wavelet domain depending on the voicing
state of the Arabic speech signal. Our proposed voiced/unvoiced decision algorithm
based on the Multi-scale Product (MP) analysis is used. The MP is based on the multiplication of wavelet transform coefficients at three successive dyadic scales. Then,
we apply a denoising technique based on the thresholding of the discrete wavelet
transform coefficients. The threshold values change either when the signal is voiced
or unvoiced. Further, a subspace decomposition-based post-processing technique is
implemented. The Fast Fourier Transform (FFT) of the obtained frames is decomposed
into three subspaces: sparse, low rank, and the remainder noise components.
Experimental results show that the proposed approach outperforms the compared
speech enhancement methods for noise-corrupted Arabic speech at low levels of
SNR. Beside, we present the evaluation results for automatic recognition on enhanced
Arabic speech signal. We reconstitute the clean Arabic speech from noisy
observations based on a sparse imputation technique. It employs a non-parametric
model and finding the sparsest combination of exemplars that jointly approximate
the reliable features of a noisy Arabic utterance.},
isbn = {978-3-959080-92-7},
issn = {0940-6832},
keywords = {Poster},
url = {https://www.essv.de/pdf/pdf/2017_50_57.pdf},
}