肌电图
语音识别
计算机科学
面部肌电图
隐马尔可夫模型
人工智能
模式识别(心理学)
物理医学与康复
医学
作者
Szu‐Chen Stan Jou,Tanja Schultz,Matthias Walliczek,Florian Kraft,Alex Waibel
标识
DOI:10.21437/interspeech.2006-212
摘要
We present our research on continuous speech recognition of the surface electromyographic signals that are generated by the human articulatory muscles. Previous research on electromyographic speech recognition was limited to isolated word recognition because it was very difficult to train phoneme-based acoustic models for the electromyographic speech recognizer. In this paper, we demonstrate how to train the phoneme-based acoustic models with carefully designed electromyographic feature extraction methods. By decomposing the signal into different feature space, we successfully keep the useful information while reducing the noise. Additionally, we also model the anticipatory effect of the electromyographic signals compared to the speech signal. With a 108-word decoding vocabulary, the experimental results show that the word error rate improves from 86.8% to 32.0% by using our novel feature extraction methods. Index Terms: speech recognition, electromyography, articulatory muscles, feature extraction.
科研通智能强力驱动
Strongly Powered by AbleSci AI