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.
Evaluation is done based on two major
components: traditional evaluation
(homework + midterm) & project

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
discussions

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

To be determined

7. Course Website

(UNDER CONSTRUCTION)