PHILLIP DELEON
University Executive Leader • Chief Research Officer • Professor of Electrical Engineering
Course

Digital Speech Processing

Speech signal analysis, coding, enhancement, recognition, and synthesis; introduction to linguistics and the human auditory and production systems.

Graduate Electrical Engineering Communications/Signal Processing Area
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Course information

New Mexico State University EE589 Digital Speech Processing (3 credits).

Prerequisites

EE395 Introduction to Digital Signal Processing, EE545 Digital Signal Processing, or equivalent.

Textbooks

  • (Required) Discrete-Time Speech Signal Processing by Thomas F. Quatieri (ISBN 0-1324-2942-X)
  • (Required) DSP Software Toolkit by Phillip L. De Leon
  • (Optional Reference) Speech Communications by Douglas O’Shaughnessy (ISBN 0-7803-3449-3)

Course Objectives

The objective of this course is to gain an understanding of digital speech processing:

  1. Production and Classification of Speech Sounds
  2. Acoustics of Speech Production
  3. Hearing
  4. Speech Analysis/Synthesis
  5. Speech Coding, Enhancement, and Modification
  6. Speaker Recognition

This objective is achieved through a graduate-level treatment of digital speech processing including both theoretical and experimental work.

Course materials

New Mexico State University EE589 Digital Speech Processing lecture notes, homework and solutions, laboratories, and exams.

This page is intended as a stable home for official course documents and high-level information.

Emphasis
Speech Production & Signal Modeling
  • Acoustic theory of speech production
  • Source–filter model of speech
  • Speech perception and auditory models
  • Time-varying nature of speech signals
  • Short-time analysis and framing
  • Parametric models of speech signals
Skills
Speech Analysis & Feature Extraction
  • Short-Time Fourier Transform (STFT) and spectrograms
  • Cepstral analysis and homomorphic processing
  • Linear Predictive Coding (LPC)
  • Pitch detection and formant estimation
  • Feature extraction (MFCCs and related features)
  • Time-frequency representations of speech
Preparation
Speech Coding, Synthesis & Recognition
  • Speech waveform and model-based coding (e.g., LPC, CELP)
  • Quantization and compression techniques
  • Speech synthesis and vocoders
  • Automatic speech recognition (ASR) fundamentals
  • Hidden Markov models (HMMs) and modern approaches
  • Applications in human–machine speech systems