Emotion Recognition using Speech Features

2013
ISBN: 978-1-4614-5142-6
Verlag: Springer

Buch, Englisch, 124 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 2175 g

Reihe: SpringerBriefs in Speech Technology

“Emotion Recognition Using Speech Features” provides coverage of emotion-specific features present in speech. The author also discusses suitable models for capturing emotion-specific information for distinguishing different emotions.  The content of this book is important for designing and developing  natural and sophisticated speech systems.
In this Brief, Drs. Rao and Koolagudi lead a discussion of how emotion-specific information is embedded in speech and how to acquire emotion-specific knowledge using appropriate statistical models. Additionally, the authors provide information about exploiting multiple evidences derived from various features and models. The acquired emotion-specific knowledge is useful for synthesizing emotions. Features includes discussion of:

• Global and local prosodic features at syllable, word and phrase levels, helpful for capturing emotion-discriminative information;
• Exploiting complementary evidences obtained from excitation sources, vocal tract systems and prosodic features in order to enhance the emotion recognition performance;
• Proposed multi-stage and hybrid models for improving the emotion recognition performance.
This brief is for researchers working in areas related to speech-based products such as mobile phone manufacturing companies, automobile companies, and entertainment products as well as researchers involved in basic and applied speech processing research.

Zielgruppe

Research

Autoren/Hrsg.

Koolagudi, Shashidhar G.

Rao, K. Sreenivasa

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

Introduction.- Speech Emotion Recognition: A Review.- Emotion Recognition Using Excitation Source Information.- Emotion Recognition Using Vocal Tract Information.- Emotion Recognition Using Prosodic Information.- Summary and Conclusions.- Linear Prediction Analysis of Speech.- MFCC Features.- Gaussian Mixture Model (GMM)

Über Autor(innen)

K. Sreenivasa Rao is at the Indian Institute of Technology, Kharagpur, India.
Shashidhar G, Koolagudi is at the Graphic Era University, Dehradun, India.