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Introduction 1.1 the Traditional Approach to Speech Processing Automatic Speech and Speaker Recognition 1.2 Potential Problems of the Probabilistic Approach Automatic Speech and Speaker Recognition

One of the most natural communication tools used by humans is their voice. It is hence natural that a lot of research has been devoted to analyzing and understanding human uttered speech for various applications. The most obvious one is automatic speech recognition, where the goal is to transcribe a recorded speech utterance into its corresponding sequence of words. Other applications include speaker recognition, where the goal is to determine either the claimed identity of the speaker (verification) or who is speaking (identification), and speaker segmentation or diarization, where the goal is to segment an acoustic sequence in terms of the underlying speakers (such as during a dialog). Although an enormous amount of research has been devoted to speech processing, there appears to be some form of local optimum in terms of the fundamental tools used to approach these problems. The aim of this book is to introduce the speech researcher community to radically different approaches based on more recent kernel based machine learning methods. In this introduction, we first briefly review the predominant speech processing approach, based on hidden Markov models, as well as its known problems; we then introduce the most well known kernel based approach, the Support Vector Machine (SVM), and finally outline the various contributions of this book.