Related Experiment Video
Updated: May 15, 2026
An Implantable System For Chronic In Vivo Electromyography
Published on: April 21, 2020
Development and evaluation of on/off control for electrolaryngeal speech via artificial neural network based on
Liang Wu1, Congying Wan, Supin Wang
1The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, PR China.
Objective:
To realize an accurate and automatic on/off control of electrolarynx (EL), an artificial neural network (ANN) was introduced for switch identification based on visual information of lips and implemented by an experimental system (ANN-EL). The objective was to confirm the feasibility of the ANN method and evaluate the performance of ANN-EL in Mandarin speech.
Study Design And Methods:
Totally five volunteers (one laryngectomee and four normal speakers) participated in the whole process of experiments. First, trained ANN was tested to assess switch identification performance of ANN method. Then, voice initiation/termination time, speech fluency, and word intelligibility were measured and compared with button-EL and video-EL to evaluate on/off control performance of ANN-EL.
Results:
The test showed that ANN method performed accurate switch identification (>99%). ANN-EL was as fast as normal voice and button-EL in onset control, but a little slower in offset control. ANN-EL could provide a fluent voice source with rare breaks (<1%) for a continuous speech. The results also indicated that on/off control performance of ANN-EL had a significant impact on perception, lowering the word intelligibility compared with button-EL. However, the words produced by ANN-EL were more intelligible than video-EL by approximately 20%.
Conclusions:
The ANN method was proved feasible and effective for switch identification based on visual information of lips. The ANN-EL could provide an accurate on/off control for fluent Mandarin speech.
