LP-Based Quality Improvement of Noisy Bone Conducted Speech

Rahman, Md. Arifour; Shimamura, Tetsuya; Makinae, Hisanori

doi:10.1541/ieejeiss.137.197

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…To improve the quality of speech, several types of algorithms have been derived for BC speech [5]- [7]. Rahman et al [8] considered a noisy environment for BC speech and derived a noise-robust LP method. However, there are few works on PLC for BC speech.…”

Section: Introductionmentioning

confidence: 99%

Packet Loss Concealment Estimating Residual Errors of Forward-Backward Linear Prediction for Bone-Conducted Speech

Yasui,

Sugiura

et al. 2024

IJACSA

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This study proposes a suitable model for packet loss concealment (PLC) by estimating the residual error of the linear prediction (LP) method for bone-conducted (BC) speech. Instead of conventional LP-based PLC techniques where the residual error is ignored, we employ forward-backward linear prediction (FBLP), known as the modified covariance (MC) method, by incorporating the residual error estimates. The MC method provides precise LP estimation for a short data length, reduces the numerical difficulties, and produces a stable model, whereas the conventional autocorrelation (ACR) method of LP suffers from numerical problems. The MC method has the effect of compressing the spectral dynamic range of the BC speech, which improves the numerical difficulties. Simulation results reveal that the proposed method provides excellent outcomes from some objective evaluation scores in contrast to conventional PLC techniques.

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Section: Introductionmentioning

confidence: 99%

Packet Loss Concealment Estimating Residual Errors of Forward-Backward Linear Prediction for Bone-Conducted Speech

Yasui,

Sugiura

et al. 2024

IJACSA

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“…However, there are few works on the spectral analysis of BC speech. Rahman et al [9] considered a noisy environment even for BC speech and derived a noise-robust linear prediction (LP) method. In [10], the spectral compensation (SC) method was derived, in which how the wide dynamic range of the BC speech power spectrum can be compacted was con-sidered.…”

Section: Introductionmentioning

confidence: 99%

Regularized Modified Covariance Method for Spectral Analysis of Bone-Conducted Speech

Ohidujjaman,

Sugiura,

Yasui

et al. 2024

Journal of Signal Processing

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We enhanced the performance of linear prediction (LP) for bone-conducted (BC) speech signals by deriving a regularized version of the modified covariance (MC) method. The spectral estimation accuracy is discussed for the purpose of assessing the performance of LP. Inherently, BC speech has an expanded spectral dynamic range that causes ill-condition in conventional spectral estimation methods. To overcome this problem, we focus on the covariance method and its modified version, the MC method. In the field of numerical analysis, we often face the situation where an ill-conditioned case occurs in finding the solution. To deal with this situation, the regularized least squares method is utilized. Motivated by this idea, we derive the regularized MC (RMC) method in this study for BC speech analysis. The RMC method has the effect of compressing the spectral dynamic range of the input speech signal, and this effect mitigates the ill-conditioned problem of LP. Through experiments, we show that the RMC method provides a more accurate spectral estimation than conventional spectral estimation methods for BC speech where synthetic and real BC speeches are considered. The performance of the RMC method is affected by the setting of the regularization parameter. A way to determine the regularization parameter in practice is experimentally derived. The RMC method with such a setting provides the best performance of spectral estimation for BC speech.

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“…This was timely matched to the requirement from real world. Then, motivated by early works [25,26] in the 2000s, where some techniques for quality improvement of BC speech were developed, BC speech has been often utilized for the purpose of speech enhancement [4][5][6][7][8][9][19][20][21] in recent years. Intelligibility of BC speech is, in many cases, lower than that of AC speech, since the high frequency components of BC speech are attenuated due to the transmission of voices on bones and skulls in human body.…”

Section: Introductionmentioning

confidence: 99%

Bone‐Conducted Speech Synthesis Based on Least Squares Method

Zhang

Sugiura

Shimamura

2022

IEEJ Transactions Elec Engng

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In this paper, we present a methodology to synthesize bone-conduced (BC) speech. Without relying on commonly used techniques for speech synthesis, we consider to transform air-conducted (AC) speech into BC speech. An infinite impulse response (IIR) filter is explored for the transformation. From the concept of system identification, the least squares (LS) method is employed and the IIR filter is designed through the recorded AC and BC speech data. By experiments, it is shown that the BC speech synthesis method is satisfactory. Filter model, order selection and stability in the methodology are discussed and noise-robustness gained by the BC speech synthesis is also investigated.

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LP-Based Quality Improvement of Noisy Bone Conducted Speech

Cited by 8 publications

References 5 publications

Packet Loss Concealment Estimating Residual Errors of Forward-Backward Linear Prediction for Bone-Conducted Speech

Packet Loss Concealment Estimating Residual Errors of Forward-Backward Linear Prediction for Bone-Conducted Speech

Regularized Modified Covariance Method for Spectral Analysis of Bone-Conducted Speech

Bone‐Conducted Speech Synthesis Based on Least Squares Method

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