Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods (Hardcover)

Speech and Speaker Recognition: Large Margin and Kernel Methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition. It presents theoretical and practical foundations of these methods, from support vector machines to large margin methods for structured learning. It also provides examples of large margin based acoustic modelling for continuous speech recognizers, where the grounds for practical large margin sequence learning are set. Large margin methods for discriminative language modelling and text independent speaker verification are also addressed in this book.

Key Features:

• Provides an up-to-date snapshot of the current state of research in this field
• Covers important aspects of extending the binary support vector machine to speech and speaker recognition applications
• Discusses large margin and kernel method algorithms for sequence prediction required for acoustic modeling
• Reviews past and present work on discriminative training of language models, and describes different large margin algorithms for the application of part-of-speech tagging
• Surveys recent work on the use of kernel approaches to text-independent speaker verification, and introduces the main concepts and algorithms
• Surveys recent work on kernel approaches to learning a similarity matrix from data

This book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learning fields.

這本書討論了大邊界和核方法在語音和語者識別中的應用。《語音和語者識別：大邊界和核方法》是對大邊界和核方法在語音和語者識別領域的最新進展進行研究的匯編。它介紹了這些方法的理論和實踐基礎，從支持向量機到結構化學習的大邊界方法。它還提供了基於大邊界的連續語音識別器的聲學建模示例，為實際的大邊界序列學習奠定了基礎。本書還討論了用於區分性語言建模和無文本語者驗證的大邊界方法。主要特點包括：提供了該領域研究的最新狀態的快照；涵蓋了將二元支持向量機擴展到語音和語者識別應用的重要方面；討論了用於聲學建模所需的大邊界和核方法算法；回顧了過去和現在關於區分性語言模型訓練的工作，並描述了不同的大邊界算法用於詞性標記的應用；概述了最近在無文本語者驗證中使用核方法的研究工作，並介紹了主要的概念和算法；概述了最近在從數據中學習相似性矩陣的核方法的研究工作。本書將對語音處理和機器學習領域的研究人員、從業人員、工程師和科學家感興趣。