Emotion Recognition from Speech Signals using Excitation Source and Spectral Features

被引：0

作者：

Choudhury, Akash Roy ^{[1
]}

Ghosh, Anik ^{[1
]}

Pandey, Rahul ^{[1
]}

Barman, Subhas ^{[1
]}

机构：

[1] Jalpaiguri Govt Engn Coll, Dept Comp Sci & Engn, Jalpaiguri, W Bengal, India

来源：

PROCEEDINGS OF 2018 IEEE APPLIED SIGNAL PROCESSING CONFERENCE (ASPCON) | 2018年

关键词：

Emotion Recognition; Spectral Features; Prosodic Features; Excitation Source Features; SMO; Random Forest; LINEAR PREDICTION; SPEAKER; CLASSIFICATION;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The task of recognition of emotions from speech signals is one that has been going on for a long time. In the previous works, the dominance of prosodic and spectral features have been observed when it comes to recognition of emotions. But a speech signal also consists of Source level information which gets lost during this process. In this work, we have combined several spectral features with several excitation source features to see how well the model can perform the emotion recognition task. For the task in hand we have taken 3 databases namely, Berlin Emotional Database (Berlin Emo-DB), Surrey Audio-Visual Expressed Emotion (SAVEE) Database and Toronto emotional speech set (TESS) Database. The reason behind taking these databases is that the variation they offer is effective to judge the robustness of the recognition model. We chose Sequential Minimal Optimization (SMO)and Random Forest to perform classification.

引用

页码：257 / 261

页数：5

共 26 条

[1]

[Anonymous], 9 EUR C SPEECH COMMU

[2]

[Anonymous], 2015, Language Identification Using Spectral and Prosodic Features

[3]

[Anonymous], 1993, Fundamentals of speech recognition

[4]

[Anonymous], AC SPEECH SIGN PROC

[5]

[Anonymous], CLASSIFICATION REGRE

[6]

[Anonymous], 2010, Toronto emotional speech set (tess)-younger talkerhappy

[7]

[Anonymous], 2009, THESIS

[8]

[Anonymous], 1998, Advances in Kernel Methods-Support Vector Learning

[9]

[Anonymous], 2014, INTRO AUDIO ANAL

[10] Random forests [J].

Breiman, L .

MACHINE LEARNING, 2001, 45 (01) :5-32

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