论文标题
使用支持向量机的语音情感识别
Speech Emotion Recognition using Support Vector Machine
论文作者
论文摘要
在这个项目中，我们旨在将演讲归类为四种情感之一，即悲伤，愤怒，恐惧和幸福。完成该项目的样本取自语言数据联盟（LDC）和UGA数据库。由样品确定的重要特征是能量，音高，MFCC系数，LPCC系数和扬声器速率。用于对这些情绪状态进行分类的分类器是支持向量机（SVM），这是使用两种分类策略完成的：一个对所有（OAA）（OAA）和依赖性分类。此外，在两者和LPCC和MFCC算法之间也进行了比较分析。
In this project, we aim to classify the speech taken as one of the four emotions namely, sadness, anger, fear and happiness. The samples that have been taken to complete this project are taken from Linguistic Data Consortium (LDC) and UGA database. The important characteristics determined from the samples are energy, pitch, MFCC coefficients, LPCC coefficients and speaker rate. The classifier used to classify these emotional states is Support Vector Machine (SVM) and this is done using two classification strategies: One against All (OAA) and Gender Dependent Classification. Furthermore, a comparative analysis has been conducted between the two and LPCC and MFCC algorithms as well.
