Springer Handbook of Speech Processing / Springer Handbooks (PDF)

Speech Signals

Part D: Text-to-Speech Synthesis (S. Narayanan); Part D presents different techniques for speech synthesis, including rule-based, corpus-based, and a combination of both. Linguistic analysis and prosodic processing, which are important parts of a text-to-speech (TTS) system, are reviewed. Other aspects of interest for TTS such as voice transformation and synthesis of expressive speech are also discussed.

Chap. 19 Basic Principles of Speech Synthesis

Chap. 20 Rule-Based Speech Synthesis

Chap. 21 Corpus-Based Speech Synthesis

Chap. 22 Linguistic Processing for Speech Synthesis

Chap. 23 Prosodic Processing

Chap. 24 Voice Transformation

Chap. 25 Expressive/Affective Speech Synthesis

Part E: Speech Recognition (L. Rabiner, B.-H. Juang); Part E describes the most important speech recognition technologies. The approach based on the powerful hidden Markov models is generously presented and some other promising approaches are outlined. The robustness issues concerning the acoustical environment are studied. Finally, several fundamental applications are also discussed.

Chap. 26 Historical Perspective of the Field of ASR/NLU

Chap. 27 HMMs and Related Speech Technologies

Chap. 28 Speech Recognition with Weighted Finite-State Transducers

Chap. 29 A Machine Learning Framework for Spoken-Dialog Classification

Chap. 30 Towards Superhuman Speech Recognition

Chap. 31 Natural Language Understanding

Chap. 32 Transcription and Distillation of Spontaneous Speech

Chap. 33 Environmental Robustness

Chap. 34 The Business of Speech Technologies

Chap. 35 Spoken Dialog Systems

Part F: Speaker Recognition (S. Parthasarathy); Part F develops the field of speaker recognition. It covers text-dependent and text-independent speaker recognition and their applications.

Chap. 36 Overview of Speaker Recognition

Chap. 37 Text-Dependent Speaker Recognition

Chap. 38 Text-Independent Speaker Recognition

Part G: Language Recognition (C.-H. Lee); Part G provides an overview on principles of state-of-the-art language recognition approaches. Language characterization, identification, and modeling are addressed. Vector space characterization approaches to converting speech utterances into spoken document vectors for modeling and classification are also presented.

Chap. 39 Principle of Spoken Language Recognition

Chap. 40 Spoken Language Characterization

Chap. 41 Automatic Language Recognition via Spectral and Token Based Approaches

Chap. 42 Vector Based Spoken Language Classification

Part H: Speech Enhancement (J. Chen, S. Gannot, J. Benesty); Part H develops all classical aspects of speech enhancement: noise reduction, dereverberation, echo cancellation, feedback control, and active noise control.

Chap. 43 Fundamentals of Noise Reduction

Chap. 44 Spectral Enhancement methods

Chap. 45 Echo Cancellation

Chap. 46 Dereverberation

Chap. 47 Adaptive Beamforming and Postfiltering

Chap. 48 Feedback Control in Hearing Aids

Chap. 49 Active Noise Control

Part I: Multichannel Speech Processing (J. Benesty, I. Cohen, Y. Huang); Part I presents modern aspects of multichannel processing, for acoustic scene analysis, speech acquisition and presentation, when a large number of microphones and loudspeakers are available.

Chap. 50 Microphone Arrays

Chap. 51 Time Delay Estimation and Source Localization

Chap. 52 Convolutive Blind Source Separation Methods

Chap. 53 Sound Field Reproduction

About the Authors

Subject Index

France, and Japan. He has been on the editorial board of the Journal Speech Communication, and has co-edited a special issue of the Transactions of the IEEE on Speech and Audio Processing, He is a Bell Labs Fellow, a co-recipient of a best paper award of the IEEE ASSP society, and a co-recipient of IEEE's E.E. Sumner award for 1998.

Y. Huang

Yiteng (Arden) Huang received the B.S. degree from the Tsinghua University in 1994, the M.S. and Ph.D. degrees from the Georgia Institute of Technology (Georgia Tech) in 1998 and 2001, respectively, all in electrical and computer engineering.

During his doctoral studies from 1998 to 2001, he was a research assistant with the Center of Signal and Image Processing, Georgia Tech, and was a teaching assistant with the School of Electrical and Computer Engineering, Georgia Tech. In the summers from 1998 to 2000, he worked with Bell Laboratories, Murray Hill, NJ and engaged in research on passive acoustic source localization with microphone arrays. Upon graduation, he joined Bell Laboratories as a Member of Technical Staff in March 2001. His current research interests are in acoustic signal processing and multimedia communications.

Dr. Huang is currently an Associated Editor of the EURASIP Journal on Applied Signal Processing. He is a member of the Signal Processing Theory and Methods and the Audio and Electroacoustics Technical Committees of the IEEE Signal Processing Society.