F0, LPC, and MFCC Analysis for Emotion Recognition Based on Speech

被引：2

作者：

Teixeira, Felipe L. ^{[1
,2
,3
]}

Teixeira, Joao Paulo ^{[2
,3
,4
]}

Soares, Salviano F. P. ^{[1
,5
]}

Pio Abreu, J. L. ^{[6
,7
]}

机构：

[1] Engn Dept UTAD, Sch Sci & Technol, P-5000801 Vila Real, Portugal

[2] Inst Politecn Braganca, Res Ctr Digitalizat & Intelligent Robot CEDRI, P-5300253 Braganca, Portugal

[3] Inst Politecn Braganca, Lab Sustentabilidade & Tecnol Regioes Montanha Su, P-5300253 Braganca, Portugal

[4] Inst Politecn Braganca, Appl Management Res Unit UNIAG, P-5300253 Braganca, Portugal

[5] Inst Elect & Informat Engn Aveiro IEETA, P-3810193 Aveiro, Portugal

[6] Hosp Univ Coimbra, P-3004561 Coimbra, Portugal

[7] Univ Coimbra, Fac Med, P-3000548 Coimbra, Portugal

来源：

OPTIMIZATION, LEARNING ALGORITHMS AND APPLICATIONS, OL2A 2022 | 2022年 / 1754卷

关键词：

Emotional state; Speech; SVM; FEATURES; SELECTION; CLASSIFICATION;

D O I：

10.1007/978-3-031-23236-7_27

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this work, research was done to understand what is needed to build a database to recognise emotions through speech. Some features that can highlight a good success rate for emotion recognition through speech were investigated. Also studied were some characteristics (symptoms) that can be associated with a specific emotional state. On the other hand, we also studied some features that can be used to identify some emotional states. A System Emotion Recognition (SER) was built with SVM, and the binary analysis was compared with a multi-category analysis. The binary analysis achieved an accuracy of 87.5% and the multi-class 42.6%. The parameters Fundamental Frequency-F0, Linear Predictive Coefficients (LPC), and Mel Frequency Cepstral Coeficients (MFCC) were used. The modest accuracy of this work was achieved using only F0, LPC and MFCC features.

引用

页码：389 / 404

页数：16

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