Springer Handbook of Speech Processing
This handbook plays a fundamental role in sustainable progress in speech research and development. With an accessible format and with accompanying DVD-Rom, it targets three categories of readers: graduate students, professors and active researchers in academia, and engineers in industry who need to understand or implement some specific algorithms for their speech-related products. It is a superb source of application-oriented, authoritative and comprehensive information about these technologies, this work combines the established knowledge derived from research in such fast evolving disciplines as Signal Processing and Communications, Acoustics, Computer Science and Linguistics.
Why Read This Book
You should read this handbook if you need a single, authoritative reference that spans speech acoustics, feature extraction, statistical modeling, enhancement, coding and evaluation. It combines tutorial-style background with algorithmic detail and pointers to implementations, making it useful both for learning and for implementing real systems.
Who Will Benefit
Graduate students, researchers, and industry engineers working on speech/audio algorithms, ASR, enhancement, coding or microphone-array processing who need both theory and applied algorithms.
Level: Intermediate — Prerequisites: Basic signals and systems and DSP, linear algebra, probability/statistics, and some programming experience (MATLAB/C/Python) to follow examples and code on the accompanying media.
Key Takeaways
- Implement standard feature extraction pipelines (e.g., MFCC, PLP) and understand their signal- and perceptual-theory basis.
- Apply statistical modeling techniques (GMMs, HMMs, likelihood-based methods) for recognition and speaker tasks.
- Design and evaluate speech enhancement and noise-reduction algorithms, including spectral and model-based methods.
- Develop microphone-array processing and beamforming solutions for source localization and separation.
- Understand speech coding techniques and perceptual evaluation metrics for system design and benchmarking.
- Conduct objective and subjective evaluation of speech systems and choose appropriate datasets and tools.
Topics Covered
- Preface and overview of speech processing
- Speech production and acoustic theory
- Perception and psychoacoustics for speech
- Time- and frequency-domain feature extraction (MFCC, PLP, filterbanks)
- Spectral analysis, cepstral representations, and transforms
- Statistical models for speech: GMMs, HMMs, discriminative methods
- Speech enhancement and noise reduction techniques
- Source separation and microphone-array processing (beamforming)
- Speech coding and compression
- Speaker recognition and diarization
- Speech synthesis and text-to-speech
- Evaluation metrics, corpora, and benchmarking
- Tools, datasets and implementation notes (DVD/online resources)
Languages, Platforms & Tools
How It Compares
Broader and more application-oriented than Rabiner & Juang's Fundamentals of Speech Recognition (which focuses tightly on ASR theory); complements Jurafsky & Martin by adding lower-level signal processing, enhancement and coding content.
