Prediction of Voice Fundamental Frequency and Intensity from Surface Electromyographic Signals of the Face and Neck.
Prediction of Voice Fundamental Frequency and Intensity from Surface Electromyographic Signals of the Face and Neck.
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DOI：
10.3390/vibration5040041
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发表时间：
2022-12
期刊：
影响因子：
2
通讯作者：
De Luca G
中科院分区：
文献类型：
--
作者：
Vojtech JM;Mitchell CL;Raiff L;Kline JC;De Luca G
无声语音接口（SSI）能够在没有声学信号的情况下进行语音识别和合成。然而，原型SSI未能传达韵律的表达属性，如音高和响度，导致词汇歧义。本研究的目的是确定使用表面肌电图（sEMG）作为预测韵律的连续声学估计的方法的有效性。10名参与者进行了一系列的声乐任务，包括持续的元音，短语和独白，同时记录声学数据与面部和颈部肌肉的表面肌电活动。从sEMG信号中提取的一组时域、频域和倒谱域特征用于训练深度回归神经网络，以从声学信号中预测基频和强度轮廓。基频估计的平均准确度为0.01 ST，平均精度为0.56 ST;强度估计的平均准确度为0.21 dB SPL，平均精度为3.25 dB SPL。这项工作突出了使用表面肌电信号作为检测韵律的替代手段的重要性，并显示了在未来发展中改善SSI的希望。
Silent speech interfaces (SSIs) enable speech recognition and synthesis in the absence of an acoustic signal. Yet, the archetypal SSI fails to convey the expressive attributes of prosody such as pitch and loudness, leading to lexical ambiguities. The aim of this study was to determine the efficacy of using surface electromyography (sEMG) as an approach for predicting continuous acoustic estimates of prosody. Ten participants performed a series of vocal tasks including sustained vowels, phrases, and monologues while acoustic data was recorded simultaneously with sEMG activity from muscles of the face and neck. A battery of time-, frequency-, and cepstral-domain features extracted from the sEMG signals were used to train deep regression neural networks to predict fundamental frequency and intensity contours from the acoustic signals. We achieved an average accuracy of 0.01 ST and precision of 0.56 ST for the estimation of fundamental frequency, and an average accuracy of 0.21 dB SPL and precision of 3.25 dB SPL for the estimation of intensity. This work highlights the importance of using sEMG as an alternative means of detecting prosody and shows promise for improving SSIs in future development.
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