Myo-electric signals to augment speech recognition
Myo-electric signals to augment speech recognition
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DOI：
10.1007/bf02345373
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发表时间：
2001-07-01
影响因子：
3.2
通讯作者：
Lovely, DF
中科院分区：
文献类型：
--
作者：
Chan, ADC;Englehart, K;Lovely, DF
有人提出，肌电信号可以用来增强传统的语音识别系统，以提高其性能下的声学噪声条件下（例如，在飞机驾驶舱）。进行了初步的研究，以确定存在的语音信息内的肌电信号从面部肌肉。使用嵌入飞行员氧气面罩中的Ag-AgCl按钮电极，在讲话期间记录五个表面肌电信号。还记录声学通道以使得能够分割所记录的肌电信号。这些段离线处理，使用小波变换特征集，并与线性判别分析分类。两个实验进行，使用十个单词的词汇组成的数字“零”到“九”。在第一个实验中测试了五个受试者，其中词汇不是随机的。受试者连续重复每个单词1分钟;分类误差范围从0.0%到6.1%。其中两名被试进行实验二，随机说出词汇表中的单词，分类错误率分别为2.7%和10.4%。结果表明，有很好的潜力，使用表面肌电信号，以提高传统的语音识别系统的性能。
It is proposed that myo-electric signals can be used to augment conventional speech-recognition systems to improve their performance under acoustically noisy conditions (e.g. in an aircraft cockpit). A preliminary study is performed to ascertain the presence of speech information within myo-electric signals from facial muscles. Five surface myo-electric signals are recorded during speech, using Ag-AgCl button electrodes embedded in a pilot oxygen mask, An acoustic channel is also recorded to enable segmentation of the recorded myo-electric signal. These segments are processed off-line, using a wavelet transform feature set, and classified with linear discriminant analysis. Two experiments are performed, using a ten-word vocabulary consisting of the numbers 'zero' to 'nine' . Five subjects are tested in the first experiment, where the vocabulary is not randomised. Subjects repeat each word continuously for 1 min; classification errors range from 0.0% to 6.1%. Two of the subjects perform the second experiment, saying words from the vocabulary randomly, classification errors are 2.7% and 10.4%. The results demonstrate that there is excellent potential for using surface myo-electric signals to enhance the performance of a conventional speech-recognition system.