A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are generated using the marginal distribution model.Each sub-classifier is based on different feature sets.The cascaded structure is adopted to fuse the sub-classifiers dynamically to achieve sample adaptation ability.Secondly,the effectiveness of the proposed algorithm is verified on electrocardiogram emotional signal and speech emotional signal.Emotional data including fidgetiness,happiness and sadness is collected by induction experiments.Finally,the emotion feature extraction method is discussed,including heart rate variability, the chaotic electrocardiogram feature and utterance level static feature.The emotional feature reduction methods are studied, including principle component analysis,sequential forward selection, the Fisher discriminant ratio and maximal information coefficient.The experimental results show that the proposed classification algorithm can effectively improve recognition accuracy in two different scenarios.
研究一种基于C4.5决策树的正常嗓音与甲亢嗓音识别方法,首先提取嗓音的基音频率,并获得与之相关的特征参数,同时与幅度微扰等参数共同组成特征集。采用美国凯益(KAY)公司的麻省眼耳科医院(Massachusetts eye and ear infirmary,MEEI)病理嗓音数据库嗓音数据进行识别,通过实验发现C4.5决策树方法与贝叶斯网络算法及支持向量机算法相比,识别率分别提高9%和15%,达到了84%的识别率。
