###### Abstract  
Voice control is an important way of controlling mobile devices; however, using it remains a challenge for dysarthric patients. Currently, there are many approaches, such as automatic speech recognition (ASR) systems, being used to help dysarthric patients control mobile devices. However, the large computation power requirement for the ASR system increases implementation costs. To alleviate this problem, this study proposed a convolution neural network (CNN) with a phonetic posteriorgram (PPG) speech feature system to recognize speech commands, called CNN-PPG; meanwhile, the CNN model with Mel-frequency cepstral coefficient (CNN-MFCC model) and ASR-based systems were used for comparison. The experiment results show that the CNN-PPG system provided 93.49% accuracy, better than the CNN-MFCC (65.67%) and ASR-based systems (89.59%). Additionally, the CNN-PPG used a smaller model size comprising only 54% parameter numbers compared with the ASR-based system; hence, the proposed system could reduce implementation costs for users. These findings suggest that the CNN-PPG system could augment a communication device to help dysarthric patients control the mobile device via speech commands in the future. mobile device; dysarthric speech; speech command recognition (SCR); signal processing; health care; Internet of Things (IoT); deep learning +
Footnote †: journal: Medicalworks on the recognition of dysarthric speech have been focused on acoustic modeling to obtain suitable acoustic cues for dysarthric speech. [PERSON] et al. [14] evaluate recognition performance for dysarthric speech compared with automatic speech recognition (ASR) systems based on Gaussian mixture model-hidden Markov models (GMM-HMMs) and support vector machines (SVMs) [15]. The experimental results showed that the HMM-based model may provide robustness against large-scale word-length variances; meanwhile, the SVM-based model can alleviate the effect of deletion of or reduction in consonants. [PERSON] et al. [16; 17] investigated acoustic models of GMM-HMM, conditional random field, SVM, and artificial neural networks (ANNs) [17], and the results showed that the ANNs provided higher accuracy than other models. Recently, deep learning technology has been widely used in many fields [18; 19; 20] and has proven it can provide better performance than conventional classification models in SCR tasks. For example, [PERSON] et al. [21] apply a deep neural network (DNN) with a data augmentation technique to perform ASR. They achieve better performance in the x-vector system; however, it is not helpful as an i-vector extractor. [PERSON] et al. [22] applied a multilingual Time Delay Neural Network (TDNN) system that combined acoustic modeling and language specific information to increase ASR performance. The experimental results showed that the TDNN-based ASR system achieved suitable performance, as the word error rate was 16.07% in this study. Although the ASR-based approach is a classical technology [8] in the dysarthric SCR task, other studies indicate that ASR systems still need huge improvements for severely dysarthric patients (e.g., cerebral palsy or stroke) [23; 24; 25]. This may be because ASR systems are trained without including dysarthric speech [14; 23; 26; 27]. Therefore, studies have tried to modify the approach of the ASR system to achieve higher performance. For example, [PERSON] et al. [28] suggested that a small-vocabulary speaker-dependent recognition system (i.e., the personalized SCR system in this study, which the dysarthric patients need to record their speech) can be more effective for severely dysarthric users in SCR tasks. [PERSON] et al. [29] applied the wavelet technique [30] to transform the acoustic data to achieve speech recognition. In the experiment, they found that the wavelet technique can achieve better performance than the traditional transform technique Mel-frequency cepstral coefficient (MFCC) in a voiced stops situation; however, MFCC showed better performance in other situations, such as with voiced fricatives.