摘要
针对语句之间的情感存在相互关联的特性,本文从声学角度提出了上下文动态情感特征、上下文差分情感特征、上下文边缘动态情感特征和上下文边缘差分情感特征共四类268维语音情感上下文特征以及这四类情感特征的提取方法,该方法是从当前情感语句与其前面若干句的合并句中提取声学特征,建立上下文特征模型,以此辅助传统特征所建模型来提高识别率.最后,将该方法应用于语音情感识别,实验结果表明,加入新的上下文语音情感特征后,六类典型情感的平均识别率为82.78%,比原有特征模型的平均识别率提高了约8.89%.
According to the emotional correlation among the adjective emotional sentences, this paper based on acoustic characteristics proposes four types of speech emotional contextual features including the contextual dynamic emotional feature, the contextual differ- ential emotional feature, the contextual edge dynamic emotional feature and the contextual edge differential emotional feature, totally 268-dimensions, and their extracted method. In this method, features are extracted from the combined emotional sentence, which is formed by jointing the current emotional sentence and the several sentences in front of it. Then use them to establish a Context Feature Model that assists the model which is formed by traditional features to improve recognition rate. Finally, the method is applied to speech emotion recognition. And the experimental result shows that average recognition rate of six typical emotions is 82.78% after adding the new contextual speech emotional features, which performs better than the original by 8.89%.
出处
《小型微型计算机系统》
CSCD
北大核心
2013年第6期1451-1456,共6页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(61003183)资助
江苏省自然科学基金项目(BK2011521)资助
江苏大学高级人(10JDG065)资助
关键词
声学上下文语音情感特征
情感语音合并句
模糊密度
决策融合
语音情感识别
acoustic contextual speech emotion feature
emotional speech combined sentence
fuzzy density
decision fusion
speech e-motion recognition
