Abstract
In order to collect a large number of speech signals from patients with speech disorders in real environment under the background of big data, this paper adopts compressed sensing(CS) theory and replaces Nyquist sampling theorem with sparse model. Based on the traditional CS theory, an optimized Hadamard matrix is proposed to perform incoherent measurement on the patient’s speech signal. Some blocks in the measurement matrix are subjected to Hadamard transformation and the columns are randomly exchanged, so that the elements in the matrix obey the zero mean and Normal distribution of variance. The experiment uses the speech signals of speech-impaired patients as samples, selects a subspace tracking algorithm with good reconstruction probability and guarantees, and compares the reconstruction effects of different measurement matrices. The experimental results show that the selected patient voice signals are run 1000 times respectively. Compared with the other five matrices, the optimized Hadamard matrix studied in this paper has the highest signal-to-noise ratio, the best reconstruction effect, and shorter running time.
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This Research work was supported in part by the Open Research Fund of National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University (Grant No. AE201906), in part by the Humanities and Social Science Fundation of the Higher Education Institutions of Anhui Province, China (Grant No. SK2019A0243), in part by the Natural Science Fundation of the Higher Education Institutions of Anhui Province, China (Grant No. KJ2018A0285), in part by the Teaching Research Fundation of the Higher Education Institutions of Anhui Province, China (Grant No. 2018jyxm0940)
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Ma, C., Li, F. (2021). Research on Speech Reconstruction Algorithm for Speech Disorder Patients Based on Compressed Sensing. In: Abawajy, J., Choo, KK., Xu, Z., Atiquzzaman, M. (eds) 2020 International Conference on Applications and Techniques in Cyber Intelligence. ATCI 2020. Advances in Intelligent Systems and Computing, vol 1244. Springer, Cham. https://doi.org/10.1007/978-3-030-53980-1_137
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DOI: https://doi.org/10.1007/978-3-030-53980-1_137
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