Provides the reader with a practical introduction to the wide range of important concepts that comprise the field of digital speech processing. Students of speech research and researchers working in the field can use this as a reference guide.

A study of digital speech processing, synthesis and recognition. This second edition contains new sections on the international standardization of robust and flexible speech coding techniques, waveform unit concatenation-based speech synthesis, large vocabulary continuous-speech recognition based on statistical pattern recognition, and more.

Theory and Applications of Digital Speech Processing is ideal for graduate students in digital signal processing, and undergraduate students in Electrical and Computer Engineering. With its clear, up-to-date, hands-on coverage of digital speech processing, this text is also suitable for practicing engineers in speech processing. This new text presents the basic concepts and theories of speech processing with clarity and currency, while providing hands-on computer-based laboratory experiences for students. The material is organized in a manner that builds a strong foundation of basics first, and then concentrates on a range of signal processing methods for representing and processing the speech signal.

A study of digital speech processing, synthesis and recognition. This second edition contains new sections on the international standardization of robust and flexible speech coding techniques, waveform unit concatenation-based speech synthesis, large vocabulary continuous-speech recognition based on statistical pattern recognition, and more.

Digital Speech Processing Using Matlab deals with digital speech pattern recognition, speech production model, speech feature extraction, and speech compression. The book is written in a manner that is suitable for beginners pursuing basic research in digital speech processing. Matlab illustrations are provided for most topics to enable better understanding of concepts. This book also deals with the basic pattern recognition techniques (illustrated with speech signals using Matlab) such as PCA, LDA, ICA, SVM, HMM, GMM, BPN, and KSOM.