IoT Device Control with Offline Automatic Speech Recognition on Edge Device

被引:3
|
作者
Setiawan, Panji [1 ]
Yusuf, Rahadian [1 ]
机构
[1] Inst Teknol Bandung, Sch Elect Engn & Informat, Bandung, Indonesia
关键词
IoT; Automatic Speech Recognition; Computer Vision;
D O I
10.1109/ICSET57543.2022.10010962
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Automatic speech recognition (ASR) on edge device still barely used in industry. Most of ASR such as speech-to-text commonly depend on the network presence. This is the disadvantages of using the server-based architecture that cannot always be reliable and available. In some conditions, the speech recognition need to be used in offline condition and edge-processing. Raspberry Pi, one of many edge devices will use offline ASR platform with only runs on python language. These platforms will encourage to do speech recognition in offline condition and edge device processing. Vosk, Picovoice/Cheetah, Sopare runs with python language and some of it required Word-Error-Rate (WER) on the result to validate result value. The 5 simple commands trained on each platform. Overall result, these 3 platforms are working well in offline environment with good accuracy (>80%). Vosk has around 84% accuracy, Picovoice/Cheetah has around 72% and Sopare has around 95% accuracy. Picovoice/Cheetah need an internet connection at first application startup.
引用
收藏
页码:111 / 115
页数:5
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