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Spectral Analysis of Bone-conducted Speech Using Modified Linear Prediction

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dc.contributor.author Ohidujjaman
dc.contributor.author Hasan, Mahmudul
dc.contributor.author Zhang, Shiming
dc.contributor.author Huda, Mohammad Nurul
dc.contributor.author Uddin, Mohammad Shorif
dc.date.accessioned 2024-12-04T06:39:38Z
dc.date.available 2024-12-04T06:39:38Z
dc.date.issued 2024-10-16
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/13587
dc.description.abstract This paper improves the performance of linear prediction (LP) in precise spectral estimation of bone-conducted (BC) speech. Inherently, BC speech contains a wide spectral dynamic range that causes ill conditioning in the autocorrelation (ACR) method and its variants, where the Levinson–Durbin (L–D) algorithm is commonly implemented. Instead of the conventional LP-based spectral estimation methods, we utilize the covariance-based method, specifically the modified covariance (MC) method, where the orthogonal decomposition algorithm is deployed. In this paper, we derive the MC method from the least squares (LS) technique for BC speech analysis. The MC method reduces the eigenvalue expansion that compresses the spectral dynamic range of the BC speech signal. The effect of spectral dynamic range compression declines the ill-conditioned properties of LP. Through the proposed method using synthetic BC speech, the resulting power spectrum provides more accurate peaks than the conventional methods. The validity of the proposed method is also analyzed by inspecting real BC speech. This study reveals the utmost use of BC speech in speech processing systems. The experimental results demonstrate that the proposed method provides more accurate spectral estimation for synthetic and real BC speeches compared with conventional spectral estimation methods. en_US
dc.language.iso en_US en_US
dc.publisher Springer Nature en_US
dc.subject Prediction en_US
dc.subject Spectral analysis en_US
dc.title Spectral Analysis of Bone-conducted Speech Using Modified Linear Prediction en_US
dc.type Article en_US


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