###### Abstract
[PERSON] et al. [8] used the best-performing set MFCC [31; 32] with artificial neural networks to perform speaker-independent ASR. The experiment results showed an average 68.4% word recognition rate with the dysarthric speaker-independent ASR model and 95.0% word recognition rate in speaker-dependent ASR systems. [PERSON] et al. [21] used the data augmentation approach called SpecAugment to improve the ASR performance, and the results showed that this approach could achieve 6.8% world error rate (WER). [PERSON] et al. [25] applied cycle-consistent adversarial training methods to improve dysarthric speech. They achieved a lower WER (33.4%) compared with automatic speech recognition of the generated utterance on a held-out test set. Recognition performance, such as with the above systems, allows users to individually train a system using their speech, thus making it possible to account for the variations in dysarthric speech [28]. Although these approaches can provide suitable performance in this task, there are some issues to overcome, including privacy (i.e., the recorded data uploaded to the server in most ASR systems) and higher computing power needed to use the ASR system. Thus, edge computing-based SCR systems, such as those using acoustic pattern recognition technologies [33; 34], are the other approaches selected in this application task. Recent studies have found that deep learning-based acoustic pattern recognition approaches [33; 34], such as the DNN [35; 36] and convolution neural network (CNN) [37; 38] models, with MFCC features provide suitable performance in the dysarthric SCR task. More specifically, one-dimensional waveform signals were preprocessed by the MFCC feature extraction unit to obtain the two-dimensional spectrographic images used to train the CNN model. Following this, the trained CNN model was used to predict results from these two-dimensional spectrographic images in the application phase. Currently, this approach,called CNN-MFCC in this study, is widely used in speech or acoustic event detection tasks. For example, [PERSON] et al. [39] used the CNN-MFCC structure to predict the tones of Mandarin from input speech, and the results showed that this approach provided higher accuracy than classical approaches. [PERSON] et al. [40] applied the CNN-MFCC structure for the automatic classification of heart sounds, and the results suggested that this structure can also provide suitable performance in this application task. [PERSON] et al. [41] used a similar concept to CNN-MFCC in a partial discharge recognition task, and the results showed that the MFCC and CNN may be a promising event recognition method for this application too. The structure of CNN-MFCC be used to help dysarthric patients. [PERSON] et al. [42] proposed a robust feature extraction method using a CNN model, which extracted the disordered speech features from a segment MFCC map. The experiment results of this study showed that the CNN-based feature extraction from the MFCC map provided better word-recognition results than other conventional feature extraction methods. More recently, [PERSON] et al. [43] proposed an empirical model decomposition and Hurst-based model selection (EMDH)-CNN system to improve the recognition of dysarthric speech. The results showed that the proposed system provided higher accuracy than the hidden Markov with Gaussian Mixture model and the CNN model by 20.72% and 9.95%, respectively. From the above studies, we infer that a robust speech feature can benefit the acoustic pattern recognition system in the application of a dysarthric patient SCR task. Recently, a novel speech feature, the phonetic posteriorgram (PPG), was proposed; this is a time-versus-class vector that expresses the posterior probabilities of phonetic classes for a specific timeframe (for a detailed description, refer to 2.2). Many studies' results show that the PPG feature can benefit many speech signal-processing tasks. For example, [PERSON] et al.