Discrete-Time Speech Signal Processing: Principles and Practice
Essential principles, practical examples, current applications, and leading-edge research.
In this book, Thomas F. Quatieri presents the field's most intensive, up-to-date tutorial and reference on discrete-time speech signal processing. Building on his MIT graduate course, he introduces key principles, essential applications, and state-of-the-art research, and he identifies limitations that point the way to new research opportunities.
Quatieri provides an excellent balance of theory and application, beginning with a complete framework for understanding discrete-time speech signal processing. Along the way, he presents important advances never before covered in a speech signal processing text book, including sinusoidal speech processing, advanced time-frequency analysis, and nonlinear aeroacoustic speech production modeling. Coverage includes:
- Speech production and speech perception: a dual view
- Crucial distinctions between stochastic and deterministic problems
- Pole-zero speech models
- Homomorphic signal processing
- Short-time Fourier transform analysis/synthesis
- Filter-bank and wavelet analysis/synthesis
- Nonlinear measurement and modeling techniques
The book's in-depth applications coverage includes speech coding, enhancement, and modification; speaker recognition; noise reduction; signal restoration; dynamic range compression, and more. Principles of Discrete-Time Speech Processing also contains an exceptionally complete series of examples and Matlab exercises, all carefully integrated into the book's coverage of theory and applications.
Why Read This Book
You should read this book if you want a rigorous, unified account of modern speech processing that bridges principled theory and practical algorithms. You will get mathematically precise treatments of linear prediction, cepstral methods, pitch and formant estimation, time-frequency approaches, and pointers to current research and implementation issues.
Who Will Benefit
Graduate students, R&D engineers, and researchers working on speech/audio signal processing, coding, and analysis who need a deep, algorithmic and statistical foundation.
Level: Advanced — Prerequisites: Solid background in signals and systems, discrete-time Fourier analysis, linear algebra, and probability/statistics; prior exposure to basic DSP (e.g., filtering, spectral estimation).
Key Takeaways
- Implement linear predictive (LPC/AR) analysis and synthesis for speech modeling and compression
- Estimate pitch (fundamental frequency) and track voicing robustly using time- and frequency-domain methods
- Apply cepstral, homomorphic, and spectral analysis techniques for formant estimation and feature extraction
- Design and evaluate parametric and nonparametric spectral estimators and formant-tracking algorithms
- Employ time-frequency representations and adaptive/statistical methods to model speech nonstationarity
Topics Covered
- Introduction: speech production and analysis framework
- Discrete-time models for speech: source–filter and stochastic models
- Linear prediction and AR modeling
- Autocorrelation, covariance methods, and parameter estimation
- Cepstral and homomorphic processing
- Pitch and fundamental frequency estimation
- Formant estimation and vocal-tract modeling
- Spectral estimation and advanced analysis methods
- Time-frequency analysis and nonstationary signal modeling
- Adaptive and statistical methods for tracking speech parameters
- Speech coding, synthesis, and applications
- Research topics, limitations, and directions
Languages, Platforms & Tools
How It Compares
More mathematically rigorous and up-to-date than Rabiner & Schafer's classic treatments, and complementary to the more application-oriented 'Speech and Audio Signal Processing' by Gold & Morgan.
