Multimodal Data Fusion of Electromyography and Acoustic Signals for Thai Syllable Recognition

Abstract

Speech disorders such as dysarthria are common and frequent after suffering a stroke. Speech rehabilitation performed by a speech-language pathologist is needed to improve and recover. However, in Thailand, there is a shortage of speech-language pathologists. In this paper, we present a syllable recognition system, which can be deployable in a speech rehabilitation system to provide support to the limited speech-language pathologists available. The proposed system is based on a multimodal fusion of acoustic signal and surface electromyography (sEMG) collected from facial muscles. Multimodal data fusion is studied to improve signal collection under noisy situations while reducing the number of electrodes needed. The signals are simultaneously collected while articulating 12 Thai syllables designed for rehabilitation exercises. Several features are extracted from sEMG signals and five channels are studied. The best combination of features and channels is chosen to be fused with the mel-frequency cepstral coefficients extracted from the acoustic signal. The feature vector from each signal source is projected by spectral regression extreme learning machine and concatenated. Data from seven healthy subjects were collected for evaluation purposes. Results show that the multimodal fusion outperforms the use of a single signal source achieving up to ∼ 98% of accuracy. In other words, an accuracy improvement up to 5% can be achieved when using the proposed multimodal fusion. Moreover, its low standard deviations in classification accuracy compared to those from the unimodal fusion indicate the improvement in the robustness of the syllable recognition.

Hand Recognition System Based on Electromyography for Real-Time Hand Prosthetic Control

“…The normalized data is divided into two parts including training and testing data and the ratio of training and testing data is 80 to 20, respectively. Next, the 10-fold cross validation will divide the training data into 10 subsets, with nine subsets used for learning and another subset used for classifier testing [21]. This process is implemented 10 times, which each subset serving as the testing data.…”