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.

This book discusses human-machine interactions, specifically focusing on making them as natural as human-human interaction. It is based on the premise that to get the right connect between human and machines, it is essential to understand not only the behavior of the person interacting with the machine, but also the limitations of the technology. Firstly, the authors review the evolution of language as a spontaneous, natural phenomenon in the overall scheme of the evolutionary development of living beings. They then go on to examine the possible approaches to understanding and representing the meaning and the common aspects of human-human and human-machine interactions, and introduce the keyconcept-keyword (also called minimal parsing) approach as a convenient and realistic way to implement usable human-machine interface (HMI) systems. For researchers looking for practical approaches, way beyond the realms of theory, this book is a must read.

The book focuses on the part of the audio conversation not related to language such as speaking rate (in terms of number of syllables per unit time) and emotion centric features. This text examines using non-linguistics features to infer information from phone calls to call centers. The author analyzes "how" the conversation happens and not "what" the conversation is about by audio signal processing and analysis.

This book discusses human-machine interactions, specifically focusing on making them as natural as human-human interaction. It is based on the premise that to get the right connect between human and machines, it is essential to understand not only the behavior of the person interacting with the machine, but also the limitations of the technology. Firstly, the authors review the evolution of language as a spontaneous, natural phenomenon in the overall scheme of the evolutionary development of living beings. They then go on to examine the possible approaches to understanding and representing the meaning and the common aspects of human-human and human-machine interactions, and introduce the keyconcept-keyword (also called minimal parsing) approach as a convenient and realistic way to implement usable human-machine interface (HMI) systems. For researchers looking for practical approaches, way beyond the realms of theory, this book is a must read.