Enabling Intelligent Environment by the Design of Emotionally Aware Virtual Assistant: A Case of Smart Campus
| Autor: | Da-Sheng Lee, Po Sheng Chiu, Ching-Hui Chen, Jia Wei Chang, Ming Che Lee |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science Interface (computing) Big data convolutional neural network Cloud computing smart campus 02 engineering and technology Augmented reality computer.software_genre Human–computer interaction Web page Health care 0202 electrical engineering electronic engineering information engineering Intelligent environment General Materials Science business.industry General Engineering 020206 networking & telecommunications Static web page emotional recognition Expert system chinese word embedding 020201 artificial intelligence & image processing recurrent neural network lcsh:Electrical engineering. Electronics. Nuclear engineering business Internet of Things computer lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 62032-62041 (2020) |
| ISSN: | 2169-3536 |
| Popis: | With the advent of the 5G and Artificial Intelligence of Things (AIoT) era, related technologies such as the Internet of Things, big data analysis, cloud applications, and artificial intelligence have brought broad prospects to many application fields, such as smart homes, autonomous vehicles, smart cities, healthcare, and smart campus. At present, most university campus app is presented in the form of static web pages or app menus. This study mainly developed a Deep Neural Network (DNN) based emotionally aware campus virtual assistant. The main contributions of this research are: (1) This study introduces the Chinese Word Embedding to the robot dialogue system, effectively improving dialogue tolerance and semantic interpretation. (2) The traditional method of emotion identification must first tokenize the Chinese sentence, analyze the clauses and part of speech, and capture the emotional keywords before being interpreted by the expert system. Different from the traditional method, this study classifies the input directly through the convolutional neural network after the input sentence is converted into a spectrogram by Fourier Transform. (3) This study is presented in App mode, which is easier to use and economical. (4) This system provides a simple voice response interface, without the need for users to find information in complex web pages or app menus. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|