Research of speech emotion recognition based on embedded system

The mandarin chinese oral speeches always contain a great number of emotions, and the speech emotion recognition is a very important key step of natural language understanding. As embedded system is widly applied in every walk of life, the speech emotion recognition technology will be extensively used in the future. Since some characteristics of embedded system such as poor resource, computing capability and etc., it is a great challenge that not only satisfactory score but also well efficiency of the speech emotion recognition algorithm must be requested. The paper describes an improved DTW-based speech emotion recognition model and an elementary emotional knowledge-base for mandarin chinese oral, and shows a embedded system application with speaker-dependent connected-word and isolated-word speech emotion recognition abilities.

[1]  Kim-Fung Man,et al.  Genetic Time Warping for Isolated Word Recognition , 1996, Int. J. Pattern Recognit. Artif. Intell..

[2]  Akira Hayashi,et al.  Embedding of time series data by using dynamic time warping distances , 2006, Systems and Computers in Japan.

[3]  Seiichi Nakagawa,et al.  Detection and recognition of correction utterances on misrecognition of spoken dialog system , 2005, Systems and Computers in Japan.

[4]  Sumit Bose,et al.  An Approach to Proper Speech Segmentation for Quality Improvement in Concatenative Text-To-Speech System for Indian Languages , 2005, Int. J. Comput. Process. Orient. Lang..

[5]  Zhao Jing,et al.  Construction and Analysis of Emotional Corpus , 2008 .

[6]  John H. L. Hansen,et al.  Discrete-Time Processing of Speech Signals , 1993 .

[7]  Ronald W. Schafer,et al.  Digital Processing of Speech Signals , 1978 .

[8]  Yang Zhihao.  Automatic acquisition of emotional vocabulary based on syntax , 2009 .

[9]  Liu Qingsheng.  Research on a Speech Endpoint Detection Method , 2003 .

[10]  M.G. Bellanger,et al.  Digital processing of speech signals , 1980, Proceedings of the IEEE.

[11]  Li Deng.  Integrated optimization of dynamic feature parameters for hidden Markov modeling of speech , 1994, IEEE Signal Process. Lett..

[12]  Ben Goertzel,et al.  Mind and Computation , 1993 .

[13]  Ahmet M. Kondoz,et al.  Digital Speech: Coding for Low Bit Rate Communication Systems , 1995 .

[14]  Monica N. Nicolescu,et al.  RECOGNIZING SIMPLE HUMAN ACTIONS USING 3D HEAD MOVEMENT , 2007, Comput. Intell..