Emotion recognition of Uyghur speech using uncertain linear discriminant analysis

基于不确定性线性判别分析的维吾尔语语音情感识别（英文）

To achieve efficient a d compact low-dimensional features for speech emotion recognition,a novel featurereduction method using uncertain linear discriminant analysis is proposed.Using the same principles as for conventional linear discriminant analysis(LDA),uncertainties of the noisy or distorted input data ae employed in order to estimate maximaiy discriminant directions.The effectiveness of the proposed uncertain LDA(ULDA)is demonstrated in the Uyghur speech emotion recognition task.The emotional features of Uyghur speech,especially,the fundamental fequency and formant,a e analyzed in the collected emotional data.Then,ULDA is employed in dimensionality reduction of emotional features and better performance is achieved compared with other dimensionality reduction techniques.The speech emotion recognition of Uyghur is implemented by feeding the low-dimensional data to support vector machine(SVM)based on the proposed ULDA.The experimental results show that when employing a appropriate uncertainty estimation algorithm,uncertain LDA outperforms the conveetional LDA counterpart on Uyghur speech emotion recognition.

为了在语音情感识别中获得高效、紧凑的低维特征,提出了一种新的基于不确定线性判别分析的特征约简方法.用与传统LDA相同的原则,在最大判别方向的估计中引入带噪声或失真输入数据的不确定性.在维吾尔语语音情感识别任务上验证了不确定性判别分析的有效性.在该情感数据上,分析了维吾尔语的语音情感特征,着重对维吾尔语语音的基音频率和共振峰频率进行了详细分析.利用不确定性线性判别分析对特征维数进行了降维研究,获得了比其他的常用降维技术更好的结果.通过不确定性线性判别分析获得的低维数据供给支持向量机,实现了维吾尔语的语音情感识别.实验结果表明,采用适当的不确定性估计算法时,在维吾尔语音情感识别任务上,不确定性线性判别分析(ULDA)算法优于传统LDA降维算法.