EE 6640-0

Brief

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.

Description

Course keywords: speech processing 語音處理, automatic speech recognition 語音辨識, signal and system 訊號與系統, machine learning 機器學習, hidden markov model 隱藏馬爾可夫模型 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

Timetable

Scores

Average Percentage 85.74

Std. Deviation 6.28

Remarks

Restrictions

Compulsory

Elective

Cross Discipline

First Specialization

Second Specialization