Analyzing Emotion in Spontaneous Speech

• Κύριοι συγγραφείς: Chakraborty, Rupayan (Συγγραφέας), Pandharipande, Meghna (Συγγραφέας), Kopparapu, Sunil Kumar (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2017.
• Θέματα: Computer science.; User interfaces (Computer systems).; Artificial intelligence.; Computer simulation.; Pattern recognition.; Application software.; Computer Science.; Artificial Intelligence (incl. Robotics).; User Interfaces and Human Computer Interaction.; Simulation and Modeling.; Signal, Image and Speech Processing.; Pattern Recognition.; Computer Appl. in Social and Behavioral Sciences.

 This book captures the current challenges in automatic recognition of emotion in spontaneous speech and makes an effort to explain, elaborate, and propose possible solutions. Intelligent human–computer interaction (iHCI) systems thrive on several technologies like automatic speech recognition (ASR); speaker identification; language identification; image and video recognition; affect/mood/emotion analysis; and recognition, to name a few. Given the importance of spontaneity in any human–machine conversational speech, reliable recognition of emotion from naturally spoken spontaneous speech is crucial. While emotions, when explicitly demonstrated by an actor, are easy for a machine to recognize, the same is not true in the case of day-to-day, naturally spoken spontaneous speech. The book explores several reasons behind this, but one of the main reasons for this is that people, especially non-actors, do not explicitly demonstrate their emotion when they speak, thus making it difficult for machines to distinguish one emotion from another that is embedded in their spoken speech. This short book, based on some of authors’ previously published books, in the area of audio emotion analysis, identifies the practical challenges in analysing emotions in spontaneous speech and puts forward several possible solutions that can assist in robustly determining the emotions expressed in spontaneous speech. 

 Introduction -- Literature Survey -- A Framework for Spontaneous Speech Emotion Recognition -- Improving Emotion Classification Accuracies -- Case Studies -- Conclusions -- Appendix -- Index.