Robust Automatic Speech Recognition: A Bridge to Practical Applications (Hardcover)

Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications. The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided. The reader will:

• Gain a unified, deep and systematic understanding of the state-of-the-art technologies for robust  speech recognition
• Learn the links and relationship  between alternative technologies for  robust speech recognition
• Be able to use the  technology analysis and categorization detailed in the book to guide future technology development
• Be able to develop new noise-robust methods in the current era of deep learning for acoustic modeling in speech recognition

• The  first book that provides a comprehensive review on noise and reverberation robust speech recognition methods in the era of deep neural networks
• Connects robust speech recognition techniques to machine learning paradigms with rigorous mathematical treatment
• Provides elegant and structural ways to categorize and analyze noise-robust speech recognition techniques
• Written by leading researchers who have been actively working on the subject matter in both industrial and academic organizations for many years

《強健的自動語音識別：實用應用的橋樑》建立了一個對抗聲學環境干擾的強健自動語音識別的堅實基礎。它全面概述了過去三十年來發展起來的經典和現代的抗噪聲和混響技術，重點介紹了已被證明成功且有望進一步發展的實用方法。詳細分析了增強強健性的語音識別技術的優點和缺點。本書涵蓋了針對基於高斯混合模型和深度神經網絡的聲學模型的抗噪聲技術。此外，還提供了選擇最佳方法應用於實際應用的指南。

讀者將能夠：
- 獲得對強健語音識別技術的統一、深入和系統化的理解
- 了解強健語音識別的替代技術之間的聯繫和關係
- 利用本書中詳細的技術分析和分類指南來引導未來技術發展
- 在當前深度學習時代的語音識別中，開發新的抗噪聲方法

本書是在深度神經網絡時代提供了對噪聲和混響強健語音識別方法的全面評論的第一本書籍。它將強健語音識別技術與機器學習範式相連接，並進行了嚴謹的數學處理。本書提供了優雅且結構化的方法來分類和分析抗噪聲語音識別技術。作者是多年來在工業和學術組織中積極從事該主題研究的領先研究人員。