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Wearable flexible sensors attached on the neck have been developed to measure the vibration of vocal cords during speech. However, high-frequency attenuation caused by the frequency response of the flexible sensors and absorption of high-frequency sound by the skin are obstacles to the practical application of these sensors in speech capture based on bone conduction. In this paper, speech enhancement techniques for enhancing the intelligibility of sensor signals are developed and compared. Four kinds of speech enhancement algorithms based on a fully connected neural network (FCNN), a long short-term memory (LSTM), a bidirectional long short-term memory (BLSTM), and a convolutional-recurrent neural network (CRNN) are adopted to enhance the sensor signals, and their performance after deployment on four kinds of edge and cloud platforms is also investigated. Experimental results show that the BLSTM performs best in improving speech quality, but is poorest with regard to hardware deployment. It improves short-time objective intelligibility (STOI) by 0.18 to nearly 0.80, which corresponds to a good intelligibility level, but it introduces latency as well as being a large model. The CRNN, which improves STOI to about 0.75, ranks second among the four neural networks. It is also the only model that is able to achieves real-time processing with all four hardware platforms, demonstrating its great potential for deployment on mobile platforms. To the best of our knowledge, this is one of the first trials to systematically and specifically develop processing techniques for bone-conduction speed signals captured by flexible sensors. The results demonstrate the possibility of realizing a wearable lightweight speech collection system based on flexible vibration sensors and real-time speech enhancement to compensate for high-frequency attenuation.

Comparison of enhancement techniques based on neural networks for attenuated voice signal captured by flexible vibration sensors on throats