An Introduction to Digital Speech Processing (Foundations and Trends(r) in Signal Processing)
Introduction to Digital Speech Processing provides the reader with a practical introduction to the wide range of important concepts that comprise the field of digital speech processing. It serves as an invaluable reference for students embarking on speech research as well as the experienced researcher already working in the field, who can utilize the book as a reference guide.
Why Read This Book
You should read this book to get a practical, DSP-centered grounding in how speech is produced, analyzed, coded, and represented for recognition and synthesis. It distills decades of Rabiner's work into clear algorithmic descriptions and examples you can apply to real systems and research.
Who Will Benefit
Graduate students and practicing engineers working on speech/audio analysis, speech coding, feature extraction for recognition, or anyone needing a DSP-first treatment of speech signals.
Level: Intermediate — Prerequisites: Basic signals and systems, discrete-time Fourier transforms/FFT, and elementary linear algebra; some familiarity with basic probability/statistics is helpful for sections on modeling.
Key Takeaways
- Analyze speech in time and frequency domains using STFT, windowing, and spectral estimation techniques.
- Implement linear predictive coding (LPC) analysis and synthesis and use LPC for formant estimation and coding.
- Compute and use cepstral representations (e.g., MFCC-like features) for feature extraction and system design.
- Estimate pitch and voicing robustly with practical algorithms and understand their limitations.
- Design and understand basic speech coders and compression methods (LPC vocoder concepts, overview of CELP principles).
- Describe the essentials of statistical speech modeling used in recognition (feature pipelines and HMM basics).
Topics Covered
- Introduction and overview of speech processing
- Speech production and perception: source-filter model
- Time-domain analysis and basic DSP review
- Spectral analysis: FFT, windowing, and spectral estimation
- Autocorrelation, covariance methods, and linear predictive coding (LPC)
- Cepstrum, homomorphic processing, and liftering
- Pitch (fundamental frequency) estimation and voicing detection
- Short-time analysis, filter banks, and perceptual representations
- Feature extraction for recognition: cepstral coefficients and their processing
- Speech coding principles and LPC-based coders (vocoders and CELP overview)
- Basic statistical models for speech and an introduction to recognition
- Practical considerations, examples, and advanced topics
Languages, Platforms & Tools
How It Compares
Covers similar DSP-focused speech fundamentals as Gold & Morgan's 'Speech and Audio Signal Processing' but with Rabiner's historical perspective and emphasis on LPC and classical speech algorithms; for recognition theory at greater depth, compare with Rabiner & Juang's 'Fundamentals of Speech Recognition.'
