並列摘要
Emotional speech classification is a current area of research with wide variety of applications in intelligent human-machine interaction systems. For classifying emotional speech signals, it is quite common to use either statistical features or temporal features. This paper focuses on the data preprocessing techniques which aim to extract the most effective acoustic features to improve the performance of the emotion recognition. The limited size of existing emotional data samples, and the relative higher dimensionality have outstripped many dimensionality reduction and feature selection algorithms. Finally, we applied this technology on mobile phone.
被引用紀錄
Hsieh, B. K. Y. (2010). 嵌入式多核心系統架構上之程式設計模型及系統軟體 [doctoral dissertation, National Tsing Hua University]. Airiti Library. https://doi.org/10.6843/NTHU.2010.00686
Chou, W. T. (2011). 在嵌入式多核心平台上設計並實作具移植性的高效能核心間溝通函式庫 [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU.2011.01442
Wu, P. W. (2008). 雙核心平台上的多媒體整合架構 [master's thesis, National Tsing Hua University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0016-2002201314574330
延伸閱讀
- Yang, Y., Wang, G., & Kong, H. (2009). Self-Learning Facial Emotional Feature Selection Based on Rough Set Theory. Mathematical Problems in Engineering, 2009(), 935-950-155. https://doi.org/10.1155/2009/802932
- Cheung, C. Y. (2002). Cognition of emotion recognition [master's thesis, The University of Hong Kong]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0029-1812201200003573
- Lee, C. Y. (2011). Combining Different Levels of Features for Emotion Recognition in Speech [master's thesis, National Tsing Hua University]. Airiti Library. https://doi.org/10.6843/NTHU.2011.00046
- 何俊元（2011）。Multi-Label Classification for Emotion Document Retrieval〔碩士論文，元智大學〕。華藝線上圖書館。https://doi.org/10.6838/YZU.2011.00334
- （2018）。Emotion Recognition System。電腦與通訊，(175)，66-66。https://www.airitilibrary.com/Article/Detail?DocID=1019391x-201810-201901070034-201901070034-66-66