Digital Speech Processing Using Matlab (Signals and Communication Technology)
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.
Why Read This Book
You will get a hands-on, MATLAB-driven introduction to practical speech processing: from speech production models and spectral analysis to feature extraction and compression, all illustrated with runnable examples. The book pairs DSP fundamentals with common pattern-recognition techniques (GMM/HMM, SVM, PCA, ICA) so you can move quickly from concept to experiment and prototype systems.
Who Will Benefit
Early-career engineers, graduate students, and researchers who want a practical, MATLAB-based entry to digital speech processing, feature extraction, and pattern-recognition for speech applications.
Level: Beginner — Prerequisites: Basic signals and systems, introductory probability and linear algebra, and familiarity with MATLAB programming (scripts, vectors/matrices).
Key Takeaways
- Understand the mechanics of speech production and how to model voiced/unvoiced speech signals
- Compute and interpret time- and frequency-domain representations using FFT and spectral-analysis methods
- Extract common speech features (LPC, MFCC, formants, pitch) and implement them in MATLAB
- Design and apply digital filters and wavelet transforms for speech analysis and noise reduction
- Implement and evaluate pattern-recognition techniques for speech (PCA, LDA, ICA, SVM, HMM, GMM, neural networks) using MATLAB
- Build simple speech-compression and coding experiments and assess performance with objective measures
Topics Covered
- Introduction to Digital Speech Processing and MATLAB
- Basics of Speech Production and Perception
- Time-Domain and Frequency-Domain Analysis (FFT, Spectral Analysis)
- Linear Predictive Coding (LPC) and Formant Analysis
- Feature Extraction: MFCCs, Pitch, Energy, Delta Features
- Digital Filter Design and Wavelet-Based Analysis
- Adaptive Filtering and Noise Reduction Techniques
- Statistical Signal Processing: Estimation and Detection
- Pattern Recognition for Speech: PCA, LDA, ICA
- Classification Methods: SVM, GMM, HMM, and Neural Networks (BPN)
- Speech Compression and Coding Techniques
- MATLAB Examples, Case Studies, and Practical Experiments
Languages, Platforms & Tools
How It Compares
Covers similar practical, MATLAB-focused material to Rabiner & Juang's speech recognition texts but is more application-oriented and accessible for beginners; more MATLAB examples than the classic Rabiner & Schafer signal-processing treatments.
