Statistical Methods for Speech Recognition

Description:

"For the first time, researchers in this field will have a book that will serve as the bible' for many aspects of language and speech processing. Frankly, I can't imagine a person working in this field not wanting to have a personal copy."
-- Victor Zue, MIT Laboratory for Computer Science

This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques.

 

 

描述：
在語言和語音處理的許多方面，這本書將成為研究人員的聖經。坦白說，我無法想像在這個領域工作的人不想擁有一本個人副本。- MIT計算機科學實驗室的Victor Zue

這本書反映了數十年來有關語音識別的數學基礎的重要研究。它專注於底層的統計技術，如隱藏馬可夫模型、決策樹、期望最大化算法、信息理論的優良標準、最大熵概率估計、參數和數據聚類以及概率分布的平滑。作者的目標是在最簡單的情境中清晰地呈現這些原則，展示從真實數據中的自組織的優勢，並使讀者能夠應用這些技術。