This is the first book dedicated to uniting research related to speech and speaker recognition based on the recent advances in large margin and kernel methods. The first part of the book presents theoretical and practical foundations of large margin and kernel methods, from support vector machines to large margin methods for structured learning. The second part of the book is dedicated to acoustic modeling of continuous speech recognizers, where the grounds for practical large margin sequence learning are set. The third part introduces large margin methods for discriminative language modeling. The last part of the book is dedicated to the application of keyword spotting, speaker verification and spectral clustering. The book is an important reference to researchers and practitioners in the field of modern speech and speaker recognition. The purpose of the book is twofold; first, to set the theoretical foundation of large margin and kernel methods relevant to speech recognition domain; second, to propose a practical guide on implementation of these methods to the speech recognition domain. The reader is presumed to have basic knowledge of large margin and kernel methods and of basic algorithms in speech and speaker recognition.