基于帧时域特征和叠加滤波器增强基于肌电图的泰文数字词分类

N. Srisuwan, Michael Wand, M. Janke, P. Phukpattaranont, Tanja Schultz, C. Limsakul
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引用次数: 1

摘要

为了克服经典自动语音识别中存在的问题(例如环境噪声和隐私丢失),使用语音产生肌肉的肌电图(EMG)来代替人类语音信号。我们的目的是研究基于泰语的肌电图语音识别。在前期工作中,我们利用面部和颈部肌肉肌电图的5个通道,基于神经网络分类对11个泰语数字词进行分类,并利用15个时域和频域特征进行特征提取。对可听语音的平均准确率为89.45%,对无声语音的平均准确率为78.55%。然而,它需要增强以获得最佳效果。本文提出了一种提高基于肌电图的泰语数字词分类准确率的方法。这10名受试者以可听和无声的方式说出11个单词,同时捕捉到5个通道的肌电图信号。特征提取阶段采用基于帧的时域特征和叠加滤波器。然后,使用LDA对特征向量进行降维。分类阶段采用隐马尔可夫模型(HMM)。结果表明,采用上述特征提取、特征降维和分类技术,可使可听语音识别的平均准确率比之前的研究提高3%。我们对可听语音和无声语音的平均分类率分别为92.45%和75.73%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancement of EMG-based Thai number words classification using frame-based time domain features with stacking filter
In order to overcome a problem existing in a classical automatic speech recognition (e.g. ambient noise and loss of privacy), Electromyography (EMG) from speech production muscles was used in place of a human speech signal. We aim to investigate the EMG speech recognition based on Thai language. The earlier work, we used five channels of the EMG from the facial and neck muscles to classify 11 Thai number words based on Neural Network Classification. 15 features in time domain and frequency domain were employed for feature extraction. We obtained an average accuracy rate of 89.45% for audible speech and 78.55% for silent speech. However, it needs to be enhanced to get the best result. This paper proposes to improve an accuracy rate of EMG-based Thai number words classification. The ten subjects uttered 11 words in both an audible and a silent speech while five channels of the EMG signal were captured. Frame-based time domain features with a stacking filter was performed for feature extraction stage. After that, LDA was used to lessen a dimension of the feature vector. Hidden Markov Model (HMM) was employed in classification stage. The results show that using above techniques of feature extraction, feature dimensionality reduction and classification can improve an average accuracy rate by 3% absolute for audible speech when were compared to earlier work. We achieved an average classification rate of 92.45% and 75.73% for audible and silent speech respectively.
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