1. Course Desc<x>ription
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<br>This course introduces signal processing techniques for processing speech 
<br>signals. Course content will be split into  three major parts: speech signal 
<br>processing techniques and analysis methods, automatic speech recognition 
<br>systems, and current research trends in  speech and language processing. 
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<br>2. Required Textbooks
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<br>Lecture notes and papers provided in class
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<br>3. Reference Books
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<br>Rabiner and Schafer: Theory and Applications of Digital Speech Processing, 
<br>Prentice Hall, 2010
<br>Huang, Acero, and Hon: Spoken Language Procesing, Prentice Hall, 2001
<br>T. Quatieri: Discrete-time Speech Signal Processing, Prentice Hall, 2001
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<br>4. Teaching Methods
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<br>English teaching. Mix of lectures and labs
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<br>5. Syllabus
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<br>Overview: Course overview, Review of basic DSP
<br>Overview:  Introduction to Speech Production & Acoustics Speech Models 
<br>Speech Analysis: Short-term Time Domain Processing, Short-time Fourier 
<br>Transform
<br>Speech Analysis: All-pole model: Linear Prediction
<br>Speech Analysis: Homomorphic Signal Processing & Cepstral Analysis
<br>ASR: Intro to Automatic Speech Recognition
<br>ASR: Hidden Markov Model  
<br>ASR: Language Model
<br>ASR: Robust front-end processing, Speaker Adaptation
<br>ASR: Advanced issues in ASR
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<br>6. Evaluation/Course Materials
<br>Please see EEclass
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<br>7. AI usage
<br>It is allowed to use genAI, please clearly indicate/disclose your usage, without 
<br>such indication, it would be treated as cheating
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