Speech Processing

Here’s a complete beginner-level Speech Processing course you can deliver to your students, including structured sessions and a suggested video series.

🎓 Speech Processing Course (Beginner Level)

📚 Course Overview

This course introduces students to the fundamentals of speech processing, covering how speech signals are produced, analyzed, and used in modern applications like speech recognition and synthesis.

🎯 Learning Objectives

By the end of the course, students will:

  • Understand how speech is produced and represented digitally
  • Analyze speech signals using basic techniques
  • Extract features from audio signals
  • Understand core concepts behind speech recognition and synthesis
  • Build simple speech processing applications

🗂️ Course Structure (12 Sessions)

🔹 Module 1: Introduction & Basics

Session 1: Introduction to Speech Processing

  • What is speech processing?
  • Applications (voice assistants, transcription, biometrics)
  • Course roadmap

Session 2: Basics of Sound and Speech Signals

  • What is sound?
  • Frequency, amplitude, waveform
  • Analog vs digital signals
  • Sampling and quantization

🔹 Module 2: Speech Production & Signal Representation

Session 3: Human Speech Production

  • Vocal tract, lungs, vocal cords
  • Voiced vs unvoiced sounds
  • Phonemes

Session 4: Digital Representation of Speech

  • Sampling theorem
  • Bit depth
  • Audio file formats (WAV, MP3)

🔹 Module 3: Speech Signal Analysis

Session 5: Time-Domain Analysis

  • Waveforms
  • Energy and zero-crossing rate

Session 6: Frequency-Domain Analysis

  • Fourier Transform
  • Spectrograms (visualizing speech)

🔹 Module 4: Feature Extraction

Session 7: Introduction to Feature Extraction

  • Why features matter
  • Overview of common features

Session 8: MFCC (Mel-Frequency Cepstral Coefficients)

  • Mel scale concept
  • Steps to compute MFCC
  • Practical examples

🔹 Module 5: Speech Processing Applications

Session 9: Speech Recognition Basics

  • What is ASR (Automatic Speech Recognition)?
  • Pipeline overview

Session 10: Speech Synthesis

  • Text-to-speech basics
  • Concatenative vs neural approaches

🔹 Module 6: Practical Work & Tools

Session 11: Tools and Libraries

  • Python for speech processing
  • Libraries: Librosa, SpeechRecognition
  • Basic coding examples

Session 12: Mini Project

  • Build a simple speech recognition or audio classifier
  • Student presentations

🎥 Example Video Series (YouTube-style)

Here’s a suggested series of videos you can either create or curate:

📺 Series Title: Speech Processing for Beginners

🎬 Video 1: Introduction to Speech Processing

  • Duration: 10–15 min
  • Content:
    • What is speech processing?
    • Real-world examples (Siri, Alexa)

🎬 Video 2: Understanding Sound Waves

  • Duration: 15 min
  • Content:
    • Frequency, amplitude
    • Visualization of waveforms

🎬 Video 3: How Humans Produce Speech

  • Duration: 12 min
  • Content:
    • Vocal tract explanation
    • Voiced vs unvoiced sounds

🎬 Video 4: Digital Audio Basics

  • Duration: 15 min
  • Content:
    • Sampling and quantization
    • Audio formats

🎬 Video 5: Time vs Frequency Domain

  • Duration: 18 min
  • Content:
    • Fourier Transform explained visually
    • Spectrogram demo

🎬 Video 6: Feature Extraction (MFCC)

  • Duration: 20 min
  • Content:
    • Step-by-step MFCC explanation
    • Simple Python demo

🎬 Video 7: Introduction to Speech Recognition

  • Duration: 15 min
  • Content:
    • How machines understand speech
    • Basic pipeline

🎬 Video 8: Speech Synthesis Basics

  • Duration: 12 min
  • Content:
    • How machines generate speech
    • Examples

🎬 Video 9: Hands-on with Python

  • Duration: 20–25 min
  • Content:
    • Load audio file
    • Plot waveform and spectrogram

🎬 Video 10: Mini Project Walkthrough

  • Duration: 25 min
  • Content:
    • Build a simple speech classifier
    • Wrap-up

🧪 Suggested Assignments

  • Plot waveform of a recorded voice
  • Compute MFCC features
  • Build a simple keyword detector
  • Compare two audio signals

🧰 Tools You Can Use

  • Python
  • Jupyter Notebook
  • Librosa
  • Praat (for phonetics analysis)

📦 Optional Enhancements

  • Add quizzes after each module
  • Include lab sessions
  • Invite students to record and analyze their own speech

If you want, I can also:

  • Turn this into slides (PowerPoint)
  • Create ready-to-use Python notebooks
  • Design exams and quizzes
  • Or adapt it for engineering vs linguistics students