摘要
情感识别是智能机器人技术研究中的一个重要课题,对不同的情感识别有助于提高机器人的智能水平和与人类沟通的效果.首先采用语速、瞬时能量、瞬时过零率、共振峰和基频五种语音特征,以及正常、喜悦、愤怒、悲伤、惊讶五种情绪状态,建立语音特征与情绪的相关性模型,然后设计PNN识别算法,再通过该算法对情绪状态训练分类,最后在语音识别过程中提取的低阶特征,识别高阶情感语义.通过实验效果对比分析,其平均识别率可以达到82.58%,优于HMM的82.2%,主成分析的66.16%和多元回归分析的62.68%,可以得出该模型对语音情感识别有较好的识别率.
Emotion recognition is an important research topic in the intelligent robot technology, and help to improve the intelligent lev- el of the robot and the effect of communicating with human. In the first place, this paper analyzes the five kinds of voice features, in- cluding instantaneous energy,instantaneous speed,zero crossing rate, fundamental frequency resonance peak）, as well as five kinds of mood, （ including normal, joy, anger, sadness, surprise ）, to establish the correlation model of emotional voice features. In the next place, we design PNN recognition algorithm, and train it by the emotional state for the classification. Finally the low order feature is extracted and high order emotion semantic is identified in the process of voice recognition. Through the comparative analyses of experi- ments effects,the average recognition rate can reach 82. 58% ,that better than the 82. 2% of HMM,the 66. 16% of main analysis,and the 62. 68% of multiple regression analysis, so this model has good recognition rate of voice emotion recognition.
出处
《小型微型计算机系统》
CSCD
北大核心
2016年第2期385-388,共4页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(61072087)资助
关键词
情感计算
语音识别
PNN
贝叶斯分类
affective computting
voice recognition
PNN
bayes classification
