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E-raamat: Acoustic Modeling for Emotion Recognition

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Acoustic Modeling for Emotion RecognitionSuurem pilt
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This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications – gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.

Arvustused

The aim of this book is to bring out various features through speech processing, and use them in an acoustic model to recognize the emotion conveyed by the person. the monogram looks concise and interesting and should be of interest to postgraduates and researchers in speech processing. (Soubhik Chakraborty, Computing Reviews, April, 2016)

1 Introduction
1(6)
1.1 Speech Signal Representation
1(1)
1.2 Acoustic Signal Basics
2(2)
1.3 Different Perspectives of Emotion
4(1)
1.3.1 Physiology of Emotion
5(1)
1.3.2 Computer Science Based Emotion
5(1)
1.4 Applications of Speech Emotion Recognition
5(1)
1.5 Book Organization
6(1)
2 Emotion Recognition Using Prosodic Features
7(10)
2.1 Introduction
7(1)
2.2 Pre-Processing
8(3)
2.2.1 Filtering
9(1)
2.2.2 Framing
9(1)
2.2.3 Windowing
10(1)
2.3 Extraction of Prosodic Features
11(4)
2.3.1 Zero Crossing Rate
11(1)
2.3.2 Short Time Energy
12(1)
2.3.3 Pitch
12(3)
2.4 Importance of Prosodic Features
15(2)
3 Emotion Recognition Using Spectral Features
17(10)
3.1 Introduction
17(1)
3.2 Extraction of Spectral Features
17(7)
3.2.1 MelFrequency Cepstral Coefficients (MFCC)
17(5)
3.2.2 Linear Prediciton Cepstral Coefficients (LPCC)
22(2)
3.2.3 Formant Features
24(1)
3.3 Importance of Spectral Features
24(3)
4 Feature Fusion Techniques
27(6)
4.1 Introduction
27(1)
4.2 Multi Modal Feature Fusion
28(5)
4.2.1 Adaptive and Non Adaptive Feature Fusion
28(2)
4.2.2 Feature Level Feature Fusion
30(1)
4.2.3 Decision Level Feature Fusion
30(3)
5 Emotional Speech Corpora
33(4)
5.1 Introduction
33(1)
5.2 Overview of Emotional Speech Databases
33(1)
5.3 Berlin Emotional Speech Database
34(1)
5.4 Spanish Emotional Speech Database
34(1)
5.5 Real Time Emotional Database of a Driver
35(2)
6 Classification Models
37(18)
6.1 Introduction
37(1)
6.2 General Classification of Classification Models
37(2)
6.2.1 Statistical Approach
38(1)
6.2.2 Syntactic or Structural Approach
38(1)
6.2.3 Template Matching
38(1)
6.2.4 Neural Networks
38(1)
6.3 Selected Classification Models
39(9)
6.3.1 Linear Discriminant Analysis
40(4)
6.3.2 Regularized Discriminant Analysis
44(1)
6.3.3 Support Vector Machine
45(2)
6.3.4 K-Nearest Neighbor
47(1)
6.4 Distance Measures
48(5)
6.4.1 Euclidian Distance
48(1)
6.4.2 Standardized Euclidian Distance
49(1)
6.4.3 Mahalanobis Distance
49(1)
6.4.4 Cityblock Distance
49(1)
6.4.5 Minkowski Distance
50(1)
6.4.6 Cosine Distance
50(1)
6.4.7 Hausdorff Distance
51(1)
6.4.8 The Directed Hausdorff Distance
51(2)
6.5 Summary
53(2)
7 Comparative Analysis of Classifiers in Emotion Recognition
55(6)
7.1 Introduction
55(1)
7.2 Emotions Used in This Work
55(1)
7.3 Analysis of Results with Each Emotion for Each Classifier
55(1)
7.4 Analysis of Results with Confusion Matrices
56(2)
7.5 Brief Overview of ROC Curves
58(2)
7.5.1 Analysis of Results with ROC Curves
59(1)
7.6 Summary
60(1)
8 Summary and Conclusions
61(2)
8.1 Summary of the Present Work
61(1)
8.2 Conclusion from the Present Work
62(1)
8.3 Scope and Future Work
62(1)
References 63
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