Enhancement of Speech Signal Segmentation Using Teager Energy Operator

Alimuradov, Alan K.

doi:10.1109/dspa51283.2021.9535805

2021 23rd International Conference on Digital Signal Processing and Its Applications (DSPA) 2021

DOI: 10.1109/dspa51283.2021.9535805

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Enhancement of Speech Signal Segmentation Using Teager Energy Operator

Alan K. Alimuradov

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Cited by 2 publications

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Unvoiced and Voiced Speech Segmentation Based on the Dimension of Signal Local Linear Manifold

Liu

Zhuang

et al. 2022

Wseas Transactions on Signal Processing

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A new method of unvoiced and voiced speech segmentation is proposed from the perspective of local linear manifold analysis of speech signal. It is based on the estimation of the dimension of short-time linear subspace. The subspace dimensional characteristics of the single phoneme signal is studied. The local signal vector set is analyzed by using PCA algorithm to estimate the dimension of the data matrix formed by framing. The local PCA is used to analyze the speech signal to achieve the segmentation of unvoiced and voiced pronunciation. Simulation experiments prove the effectiveness of the proposed method.

show abstract

Unvoiced and Voiced Speech Segmentation Based on the Dimension of Signal Local Linear Manifold

Liu

Zhuang

et al. 2022

Wseas Transactions on Signal Processing

View full text Add to dashboard Cite

show abstract

Speech Segmentation Based on the Computation of Local Signal Manifold Dimension

Liu

Zhuang

Mastorakis

2023

Wseas Transactions on Systems

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A new computational method of unvoiced and voiced speech segmentation is proposed from the perspective of local linear manifold analysis of speech signals. It is based on the estimation of the dimension of short-time linear subspace. The subspace dimensional characteristics of the single phoneme signal are studied. The local signal vector set is analyzed by using the PCA algorithm to estimate the dimension of the data matrix formed by framing. The local PCA is used to analyze the speech signal to achieve the segmentation of unvoiced and voiced pronunciation. Simulation experiments prove the effectiveness of the proposed method.

show abstract

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Enhancement of Speech Signal Segmentation Using Teager Energy Operator

Cited by 2 publications

References 10 publications

Unvoiced and Voiced Speech Segmentation Based on the Dimension of Signal Local Linear Manifold

Unvoiced and Voiced Speech Segmentation Based on the Dimension of Signal Local Linear Manifold

Speech Segmentation Based on the Computation of Local Signal Manifold Dimension

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