Why Read This Book

You should read this classic if you want a rigorous, engineering-focused foundation in how speech signals are analyzed and processed: you will learn the signal models and algorithms behind LPC, spectral and cepstral analysis, pitch estimation, and filtering. The book connects DSP theory to practical speech tasks, making it a go-to reference for building and understanding speech-processing systems.

Who Will Benefit

Engineers or graduate students with some DSP background who are developing or researching speech/audio processing, speech coding, or speech-related signal algorithms.

Level: Advanced — Prerequisites: Undergraduate-level signals and systems, basic digital signal processing (Fourier transforms, sampling, z-transform), linear algebra, and probability/statistics.

Key Takeaways

• Explain models of speech production and perception and translate them into signal-processing tasks
• Implement linear predictive coding (LPC) to model, analyze, and compress speech signals
• Perform spectral and cepstral analysis using FFT techniques to extract speech features
• Design and apply digital filters and adaptive-filter algorithms for noise reduction and enhancement
• Detect pitch and estimate formants robustly using time- and frequency-domain methods
• Apply statistical signal-processing techniques for parameter estimation used in coding and recognition

Topics Covered

1. Introduction and overview of speech processing
2. Speech production and perception models
3. Discrete-time representation of speech signals
4. Time-domain analysis and preprocessing (windowing, framing, preemphasis)
5. Spectral analysis and the FFT for speech
6. Linear prediction theory and LPC implementation
7. Cepstral and homomorphic signal processing
8. Pitch detection, voicing analysis, and fundamental frequency estimation
9. Formant analysis and short-time spectral methods
10. Digital filter design for speech applications
11. Adaptive filtering and noise cancellation (LMS, RLS approaches)
12. Statistical signal processing methods for speech parameter estimation
13. Applications: speech coding, synthesis, and recognition
14. Appendices: mathematical and algorithmic references

Languages, Platforms & Tools

How It Compares

More focused on speech-specific DSP than Oppenheim & Schafer's Discrete-Time Signal Processing (which covers general DSP theory), and it complements Rabiner & Juang's Fundamentals of Speech Recognition by providing deeper signal-processing foundations rather than recognition architectures.