Using speech recognition to analyze the physical condition of the elderly and its application in telemedicine

Jianyu Wang ； Lingjing Yu ； Ming Huang ； Runchuan Feng ； Yiyang He

《International Core Journal of Engineering》 7卷12期 (2021/12) Pp. 619-626

https://doi.org/10.6919/ICJE.202112_7(12).0086

摘要

With the continuous development of science and technology and the increasing importance of the elderly to their own health, telemedicine has been favored by more and more people and has gradually become a research hotspot in the medical field. Aiming at the lack of accuracy of telemedicine, this article starts with the basic situation of intelligent speech recognition and explains its application methods and methods; it uses the extraction of Mel frequency cepstrum parameters (MFCC) and dynamic time warping (DTW) algorithm to pair. The simulation experiment results show that the method can accurately match the corresponding semantics in the reference speech database. This provides a certain reference for improving the application level of the telemedicine system.

關鍵字

Telemedicine ； intelligent speech recognition ； Mel frequency cepstrum parameters ； dynamic time warping

參考文獻

Hantke Simone,Weninger Felix,Kurle Richard,Ringeval Fabien,Batliner Anton,Mousa Amr El-Desoky,Schuller Björn. I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance.[J]. PloS one,2016,11(5).

Yuchen Zhou, Yu Zhang. Article: Analysis onthe status quo and countermeasures of healthy old-age care in China[J]. Chronic Diseases Prevention Review,2019,3(11).

Hao Ouya, Wu Xuan, Liu Rongkai. The development status and application prospects of intelligent speech recognition technology [J]. Electroacoustic Technology, 2020, 44(03): 24-26.

Zhang Xin, Wang Xiaohua. Research on the problems and countermeasures of telemedicine services under the background of "Internet +"[J]. Satellite TV and Broadband Multimedia, 2019(15): 34-35.

Cang Yan, Luo Shunyuan, Qiao Yulong. Pig voice classification based on deep neural network[J].Journal of Agricultural Engineering,2020, 36(9): 195-204.