Speech Recognition for People with Dysphasia Using Convolutional Neural Network

• Main Authors: Lin, Bo-Yu, Huang, Hung-Shing, Sheu, Ruey-Kai, Chang, Yue-Shan
• Format: Conference Proceeding
• Language: English
• Subjects: Convolutional Neural Networks; Deep learning; Diseases; Dysphasia; Mel frequency cepstral coefficient; Speech recognition; Training
• ISSN: 2577-1655
• DOI: 10.1109/SMC.2018.00372

As the advance of technology, it is increasingly speech recognition tools on mobile devices, such as Google Voice and Apple Siri, those have been widely used and have high recognition rate of people's speech. However, these speech recognition tools cannot work well for people with the disease of "Dysphasia" and has very low recognition rate. It is important issue to develop a speech recognition toot for dysphasia, such as Cerebral Palsy(CP) and Amyotrophic Lateral Sclerosis(ALS), to assist those people communicating with others well. Recently, there are various open source programs has been announced, such as Google's Tensorflow, which is used to develop speech recognition based on Deep Neural Networks (DNNs). In this paper, we propose a Convolutional Neural Networks (CNNs) model to perform speech recognition for ALS. The model consists of two hidden CNN layers, each includes one CNN, Rectified Linear Unit (ReLU), Dropout Unit, and MaxPooling Layer. We implement the CNN for dysphasia speech recognition using Google's Tensorflow and collect 33 pronunciations from a dysphasia, each pronunciation at least has 350 training voice files. The highest accuracy is about 63% for one word. And it will be up to totally 94% for top five words. The result shows that it can effectively recognize speech for dysphasia. It can be expected to construct a speech recognition system for assisting dysphasia communicating with others.