Master practical knowledge and skills for handling speech data, from the fundamentals of speech signal processing to deep learning-based speech recognition, speech synthesis, and speech classification
Series Overview
This series is a comprehensive 5-chapter practical educational content that teaches the theory and implementation of speech processing and speech recognition progressively from fundamentals.
Speech Processing and Speech Recognition are critical technologies used in various aspects of modern society, including voice assistants (Siri, Alexa, Google Assistant), automatic subtitle generation, speech translation, call center automation, and voice search. You will systematically understand the complete picture of speech AI, from digital audio fundamentals to acoustic features like MFCC and mel-spectrograms, traditional HMM-GMM models, state-of-the-art deep learning-based speech recognition (Whisper, Wav2Vec 2.0), speech synthesis (TTS, Tacotron, VITS), and applied technologies such as speaker recognition, emotion recognition, and speech enhancement. Learn the principles and implementation of cutting-edge models developed by Google, Meta, and OpenAI, and acquire practical skills using real speech data. Implementation methods using major libraries such as librosa, torchaudio, and Transformers are provided.
Features:
- ✅ From Theory to Practice: Systematic learning from acoustic fundamentals to state-of-the-art deep learning models
- ✅ Implementation-Focused: Over 50 executable Python/librosa/PyTorch code examples
- ✅ Practically-Oriented: Hands-on projects using real speech data
- ✅ Latest Technology: Implementation using Whisper, Wav2Vec 2.0, VITS, and Transformers
- ✅ Practical Applications: Implementation of speech recognition, speech synthesis, speaker recognition, and emotion recognition
Total Study Time: 5-6 hours (including code execution and exercises)
How to Study
Recommended Study Order
For Beginners (no knowledge of speech processing):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (all chapters recommended)
- Time Required: 5-6 hours
For Intermediate Learners (with machine learning experience):
- Chapter 1 → Chapter 3 → Chapter 4 → Chapter 5
- Time Required: 4-5 hours
For Specific Topic Enhancement:
- Speech Signal Processing & MFCC: Chapter 1 (focused study)
- HMM & GMM: Chapter 2 (focused study)
- Deep Learning Speech Recognition: Chapter 3 (focused study)
- Speech Synthesis & TTS: Chapter 4 (focused study)
- Speaker Recognition & Emotion Recognition: Chapter 5 (focused study)
- Time Required: 60-80 minutes/chapter
Chapter Details
Chapter 1: Fundamentals of Speech Signal Processing
Difficulty: Intermediate
Reading Time: 60-70 minutes
Code Examples: 12
Learning Content
- Digital Audio Fundamentals - Sampling, quantization, Nyquist theorem
- Acoustic Features - MFCC, mel-spectrogram, pitch, formants
- Spectral Analysis - Fourier transform, STFT, spectrogram
- Using librosa - Audio loading, feature extraction, visualization
- Speech Preprocessing - Noise reduction, normalization, VAD (Voice Activity Detection)
Learning Objectives
- ✅ Understand the fundamental principles of digital audio
- ✅ Explain acoustic features (MFCC, mel-spectrogram)
- ✅ Understand spectral analysis methods
- ✅ Process audio data using librosa
- ✅ Implement speech preprocessing techniques
Chapter 2: Traditional Speech Recognition
Difficulty: Intermediate
Reading Time: 60-70 minutes
Code Examples: 8
Learning Content
- Speech Recognition Fundamentals - Acoustic model, language model, decoding
- HMM (Hidden Markov Model) - State transition, observation probability, Viterbi algorithm
- GMM (Gaussian Mixture Model) - Acoustic modeling, EM algorithm
- Language Model - N-gram, statistical language model, smoothing
- Evaluation Metrics - WER (Word Error Rate), CER (Character Error Rate)
Learning Objectives
- ✅ Understand the basic architecture of speech recognition
- ✅ Explain HMM principles and Viterbi algorithm
- ✅ Understand acoustic modeling with GMM
- ✅ Implement N-gram language models
- ✅ Evaluate performance using WER and CER
Chapter 3: Deep Learning-based Speech Recognition
Difficulty: Intermediate to Advanced
Reading Time: 80-90 minutes
Code Examples: 10
Learning Content
- End-to-End Speech Recognition - CTC (Connectionist Temporal Classification)
- RNN-Transducer - Streaming speech recognition, online recognition
- Transformer Speech Recognition - Self-Attention, Positional Encoding
- Whisper - OpenAI's multilingual speech recognition model, zero-shot learning
- Wav2Vec 2.0 - Self-supervised learning, speech representation learning
Learning Objectives
- ✅ Understand the principles of CTC loss function
- ✅ Implement streaming recognition with RNN-Transducer
- ✅ Understand Transformer applications in speech recognition
- ✅ Implement multilingual speech recognition with Whisper
- ✅ Learn speech representations with Wav2Vec 2.0
Chapter 4: Speech Synthesis
Difficulty: Intermediate to Advanced
Reading Time: 70-80 minutes
Code Examples: 10
Learning Content
- TTS (Text-to-Speech) Fundamentals - Phonetic conversion, prosody generation, speech synthesis
- Tacotron 2 - Seq2Seq model, Attention mechanism, mel-spectrogram generation
- FastSpeech - Non-autoregressive model, parallel generation, fast synthesis
- VITS - End-to-end TTS, variational inference, neural vocoder
- Vocoders - WaveNet, WaveGlow, HiFi-GAN
Learning Objectives
- ✅ Understand the basic architecture of TTS
- ✅ Generate mel-spectrograms with Tacotron 2
- ✅ Implement fast speech synthesis with FastSpeech
- ✅ Implement end-to-end TTS with VITS
- ✅ Generate speech waveforms with neural vocoders
Chapter 5: Speech Applications
Difficulty: Intermediate to Advanced
Reading Time: 70-80 minutes
Code Examples: 12
Learning Content
- Speaker Recognition - Speaker identification, speaker verification, x-vector, d-vector
- Emotion Recognition - Acoustic features, prosodic features, deep learning models
- Speech Enhancement - Noise reduction, beamforming, masking techniques
- Music Information Retrieval - Tempo detection, beat tracking, genre classification
- Voice Activity Detection (VAD) - WebRTC VAD, deep learning-based VAD
Learning Objectives
- ✅ Understand and implement speaker recognition methods
- ✅ Recognize emotions from speech
- ✅ Implement speech enhancement techniques
- ✅ Understand music information retrieval fundamentals
- ✅ Detect voice activity with VAD
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Explain digital audio and acoustic features like MFCC
- ✅ Understand the differences between HMM-GMM and CTC
- ✅ Explain the latest trends in deep learning speech recognition
- ✅ Understand the principles of TTS and speech synthesis
- ✅ Explain speaker recognition and emotion recognition methods
Practical Skills (Doing)
- ✅ Process audio data using librosa
- ✅ Extract MFCC and mel-spectrograms
- ✅ Implement speech recognition with Whisper
- ✅ Implement speech synthesis with VITS
- ✅ Build speaker recognition and emotion recognition models
Application Ability (Applying)
- ✅ Select appropriate speech recognition methods for projects
- ✅ Design speech data preprocessing pipelines
- ✅ Build custom speech recognition systems
- ✅ Develop speech synthesis applications
- ✅ Evaluate and improve speech AI systems
Prerequisites
To effectively study this series, it is desirable to have the following knowledge:
Required (Must Have)
- ✅ Python Fundamentals: Variables, functions, classes, NumPy, pandas
- ✅ Machine Learning Fundamentals: Concepts of training, evaluation, loss functions
- ✅ Mathematics Fundamentals: Linear algebra, probability & statistics, calculus
- ✅ Signal Processing Basics: Fourier transform concepts (recommended)
- ✅ Deep Learning Basics: CNN, RNN, Transformer fundamentals (from Chapter 3 onwards)
Recommended (Nice to Have)
- 💡 PyTorch Basics: Tensor operations, model building, training loops
- 💡 Transformers Experience: Hugging Face Transformers library
- 💡 Acoustics Knowledge: Sound waves, frequency, decibels
- 💡 Natural Language Processing: Tokenization, language models (for speech recognition)
- 💡 Time Series Data Processing: RNN, LSTM, Seq2Seq
Recommended Prior Study:
- 📚 - ML fundamental knowledge
Technologies and Tools Used
Main Libraries
- librosa 0.10+ - Speech signal processing, feature extraction
- PyTorch 2.0+ - Deep learning framework
- torchaudio 2.0+ - PyTorch audio processing library
- Transformers 4.30+ - Hugging Face, Whisper, Wav2Vec 2.0
- SpeechBrain 0.5+ - Speech processing toolkit
- Kaldi - Traditional speech recognition toolkit (reference)
- ESPnet - End-to-end speech processing toolkit
Development Environment
- Python 3.8+ - Programming language
- Jupyter Notebook / Google Colab - Interactive development environment
- NumPy 1.23+ - Numerical computing
- SciPy 1.10+ - Scientific computing
- matplotlib / seaborn - Visualization
Datasets (Recommended)
- LibriSpeech - English speech recognition benchmark
- Common Voice - Multilingual speech dataset
- LJSpeech - English speech synthesis dataset
- VCTK - Multi-speaker speech dataset
- RAVDESS - Emotional speech dataset
Let's Get Started!
Are you ready? Begin with Chapter 1 and master speech processing and speech recognition technologies!
Chapter 1: Fundamentals of Speech Signal Processing →
Next Steps
After completing this series, we recommend advancing to the following topics:
Advanced Learning
- 📚 Spoken Dialogue Systems: Voice assistants, dialogue management, NLU integration
- 📚 Multilingual Speech Processing: Cross-lingual transfer learning, low-resource language support
- 📚 Real-time Speech Processing: Streaming processing, low-latency optimization
- 📚 Speech Generation Models: Voice conversion, voice cloning, singing synthesis
Related Series
- 🎯 Natural Language Processing Introduction - Text processing, language models
- 🎯 Computer Vision Introduction - Multimodal AI
- 🎯 Transformer Architecture (Coming Soon) - Attention mechanism
Practical Projects
- 🚀 Voice Assistant - Wake word detection, speech recognition, voice response
- 🚀 Automatic Subtitle Generation System - Video speech recognition, timestamped subtitles
- 🚀 Multilingual Speech Translation App - Speech recognition → machine translation → speech synthesis
- 🚀 Emotion Recognition Call Center AI - Customer emotion analysis, quality monitoring
Update History
- 2025-10-21: v1.0 Initial release
Your journey into speech AI begins here!