1. Course Description

This course introduces signal processing techniques for processing speech
signals. Course content will be split into three major parts: speech signal
processing techniques and analysis methods, automatic speech recognition
systems, and current research trends in speech and language processing.


2. Required Textbooks

Lecture notes and papers provided in class

3. Reference Books

Rabiner and Schafer: Theory and Applications of Digital Speech Processing,
Prentice Hall, 2010
Huang, Acero, and Hon: Spoken Language Procesing, Prentice Hall, 2001
T. Quatieri: Discrete-time Speech Signal Processing, Prentice Hall, 2001

4. Teaching Methods

English teaching. Mix of lectures and labs

5. Syllabus

Overview: Course overview, Review of basic DSP
Overview: Introduction to Speech Production & Acoustics Speech Models
Speech Analysis: Short-term Time Domain Processing, Short-time Fourier
Transform
Speech Analysis: All-pole model: Linear Prediction
Speech Analysis: Homomorphic Signal Processing & Cepstral Analysis
ASR: Intro to Automatic Speech Recognition
ASR: Hidden Markov Model
ASR: Language Model
ASR: Robust front-end processing, Speaker Adaptation
ASR: Advanced issues in ASR


6. Evaluation/Course Materials
Please see EEclass

7. AI usage
It is allowed to use genAI, please clearly indicate/disclose your usage, without
such indication, it would be treated as cheating