Robust Emotion Recognition using Spectral and Prosodic Features - Tapa blanda

Sinopsis

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . 1 1.2 Emotion from psychological perspective . . . . . . . . . . . . . . . . . . . .. . 2 1.3 Emotion from speech signal perspective . . . . . . . . . . . . . . .. . . . . . . 3 1.3.1 Speech production mechanism . . . . . . . . . . . . . . . . . . . . . . . . .... 4 1.3.2 Source features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.3 System features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3.4 Prosodic features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Emotional speech databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5 Applications of speech emotion recognition . . . . . . . . . . . . . . . . . . . 8 1.6 Issues in speech emotion recognition . . . . . . . . . . . . . . . . . . . . . . . 8 1.7 Objectives and scope of the work . . . . . . . . . . . . . . . . . . . . . . . . .. 9 1.8 Main highlights of research investigations . . . . . . . . . . . . . . . . . . . 10 1.9 Brief overview of contributions in this book . . . . . . . . . . . . . . .. . . 10 1.9.1 Emotion recognition using spectral features extracted from sub-syllabic regions and pitch synchronous analysis . . . . . . . 10 1.9.2 Emotion recognition using global and local prosodic features extracted from words and syllables . . . . . . . . . . . . . . 11 1.9.3 Emotion recognition using combination of features . . . . . . . . 11 1.9.4 Emotion recognition on real life emotional speech database . 11 1.10 Organization of the book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2 Robust Emotion Recognition using Pitch Synchronous and

Sub-syllabic Spectral Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.1 Introduction . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . 15 2.2 Emotional speech corpora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.1 Indian Institute of Technology Kharagpur-Simulated Emotional Speech Corpus: IITKGP-SESC . . . . . . . . . . . . . . . 18 2.2.2 Berlin Emotional Speech Database: Emo-DB . . . . . . . . . . . . . 20 2.3 Feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.1 Linear prediction cepstral coefficients (LPCCs) . . . . . . . . . . . 21 2.3.2 Mel frequency cepstral coefficients (MFCCs) . . . . . . . . . . . . . 22 2.3.3 Formant features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.4 Extraction of sub-syllabic spectral features . . . . . . . . . . . . . . . 25 2.3.5 Pitch synchronous analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4 Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4.1 Gaussian mixture models (GMM) . . . . . . . . . . . . . . . . . . . . . . 30 2.4.2 Auto-associative neural networks . . . . . . . . . . . . . . . . . . . . . . . 31 2.5 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3 Robust Emotion Recognition using Word and Syllable Level Prosodic Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.1 Introduction . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 45 3.2 Prosodic features: Importance in emotion recognition . . . . . . . . . . 46 3.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.4 Extraction of global and local prosodic features . . . . . . . . . . . . . . . 51 3.4.1 Sentence level features . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 51 3.4.2 Word and syllab

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