<scp>Bone‐Conducted</scp> Speech Synthesis Based on Least Squares Method

Zhang, Shiming; Sugiura, Yuta; Shimamura, Tetsuya

doi:10.1002/tee.23531

Cited by 8 publications

(5 citation statements)

References 35 publications

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“…3 where AC speech yields larger values than BC speech. Low values of LAR indicate that the restored speech is more similar to the original transmitted speech [23]. The LAR distance of BC speech becomes nearer to the baseline in contrast to relative AC speech.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, in recent years BC speech, which is obtained through a BC microphone, has received attention. The AC speech is more sensitive to ambient noise whereas BC speech possesses the corresponding less sensitivity [23], since the BC microphone acquires the speech signals picked up through the speaker's face and head [24]. Recent reports show that BC speech is also useful for speech analysis such as pitch detection [25] and speaker recognition [26] in a noiseless environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Packet Loss Compensation for VoIP through Bone‐Conducted Speech Using Modified Linear Prediction

Ohidujjaman,

Yasui,

Sugiura

et al. 2023

IEEJ Transactions Elec Engng

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In this paper, we compare air‐conducted (AC) speech with bone‐conducted (BC) speech for the purpose of utilizing them in packet loss concealment (PLC) for speech quality of voice over internet protocol (VoIP). Instead of the autocorrelation method of linear prediction (LP), which was utilized in the conventional PLC techniques, we employ the modified covariance (MC) method. The MC method provides accurate LP estimation from short input data samples and avoids the numerical problem the autocorrelation method suffers from. The lost frame is compensated from both forward and backward directions in which linear gain and weighting are applied. When BC speech is used as the input speech data for PLC in the case where the speech sender is in noisy environments, BC speech behaves more accurately than the corresponding AC speech, resulting in an excellent performance of the LP‐based PLC technique. This unveils a useful use of BC speech in speech information systems. Experiments show that in severe noise environments of AC speech‐to‐noise ratio being less than 10 dB, BC speech is superior to AC speech for PLC. It is also shown that the transmitted BC speech is more accurately reconstructed for PLC than transmitted AC speech is done. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Packet Loss Compensation for VoIP through Bone‐Conducted Speech Using Modified Linear Prediction

Ohidujjaman,

Yasui,

Sugiura

et al. 2023

IEEJ Transactions Elec Engng

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“…To synthesize BC speech from AC speech, in this study, we aim to identify the coefficients α and β necessary for the synthesis process. Zhang et al [30] determined α =0.75 and β = 0.20 for male and α = 0.76 and β = 0.21 for female by identifying the transfer function in a least squares sense. Referring to the results in [30], in our study, we use each averaged value for α and β for simplicity regardless of male or female and synthesize BC speech from AC speech.…”

Section: Data Preparationmentioning

confidence: 99%

Deep-Learning-Based Speech Emotion Recognition Using Synthetic Bone-Conducted Speech

Hosain,

Sugiura,

Yasui

et al. 2023

Journal of Signal Processing

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Speech emotion recognition has drawn extensive attention in recent years. We propose deep learning (DL)-based speech emotion recognition using synthetic bone-conducted (BC) speech. In our proposed model, air-conducted(AC) speech is transformed to BC speech using an infinite impulse response (IIR) filter. Data augmentation techniques are utilized and the parameters of convolutional neural network (CNN) models are modified to enhance the accuracy of the proposed model. Simulation results demonstrate that the proposed model outperforms the existing models in terms of recognition accuracy for BC speech. The accuracy of the proposed model is 72.50% for BC speech, whereas that of the existing model is 69.83% for AC speech. This is because BC speech can enhance low-frequency components, which is important for recognizing emotions.

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“…Therefore, BC speech delivers advantages over air-conducted (AC) speech in some cases. In [1], BC speech was synthesized based on the least squares (LS) method. Pitch detection for BC speech is discussed in [2].…”

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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Bone‐Conducted Speech Synthesis Based on Least Squares Method

Cited by 8 publications

References 35 publications

Packet Loss Compensation for VoIP through Bone‐Conducted Speech Using Modified Linear Prediction

Packet Loss Compensation for VoIP through Bone‐Conducted Speech Using Modified Linear Prediction

Deep-Learning-Based Speech Emotion Recognition Using Synthetic Bone-Conducted Speech

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

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