Advanced Speech Recognition and Synthesis
Chapter 1: Foundations of Modern Speech Processing Pipelines
Advanced Audio Feature Extraction
Statistical Modeling Review for Speech
Deep Learning Architectures for Sequences
Components of ASR Systems
Components of TTS Systems
Evaluation Metrics Revisited
Chapter 2: Advanced Acoustic Modeling for ASR
Hybrid HMM-DNN Systems
Connectionist Temporal Classification (CTC)
Attention-Based Encoder-Decoder Models
RNN Transducer (RNN-T)
Transformer Architectures for ASR
Advanced Training Techniques
Decoding Algorithms Comparison
Hands-on Practical: Building an End-to-End ASR Model
Chapter 3: Language Modeling and Adaptation in ASR
Neural Language Models for ASR
Shallow Fusion and Deep Fusion
Contextual ASR
Speaker Adaptation Techniques
Environment and Channel Adaptation
Unsupervised and Semi-Supervised Learning for ASR
Multi-Lingual and Cross-Lingual ASR
Practice: Fine-tuning ASR with Adaptation Data
Chapter 4: Advanced Text-to-Speech Synthesis
Autoregressive Acoustic Models (Tacotron, Transformer TTS)
Non-Autoregressive Acoustic Models (FastSpeech, ParaNet)
Flow-Based Models for TTS
Generative Adversarial Networks (GANs) in TTS
Prosody Modeling and Control
Expressive Speech Synthesis
Voice Cloning and Conversion
Hands-on Practical: Training an Advanced TTS Model
Chapter 5: Neural Vocoders and Waveform Generation
Limitations of Traditional Vocoders
Autoregressive Waveform Models (WaveNet, WaveRNN)
Flow-Based Vocoders (WaveGlow, FloWaveNet)
GAN-Based Vocoders (MelGAN, HiFi-GAN)
Diffusion Models for Vocoding
Conditioning Neural Vocoders
Evaluation of Synthesized Audio Quality
Hands-on Practical: Using a Neural Vocoder
Chapter 6: Optimization, Deployment, and Toolkits
Model Quantization for Speech Models
Model Pruning and Sparsification
Knowledge Distillation for ASR/TTS
Optimized Inference Engines (ONNX Runtime, TensorRT)
Deployment Considerations for Streaming ASR
Deployment Considerations for Real-Time TTS
Overview of Speech Processing Toolkits (ESPnet, NeMo, Coqui)
Practice: Optimizing a Speech Model
Connectionist Temporal Classification (CTC)
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- Connectionist Temporal Classification: Labelling Sequences without Alignment, Alex Graves, Santiago Fernandez, Faustino Gomez, and Jürgen Schmidhuber, 2006 Proceedings of the 23rd International Conference on Machine Learning - ICML '06 (ACM) DOI: 10.1145/1143812.1143862 - The original foundational paper introducing the Connectionist Temporal Classification (CTC) algorithm, including its mathematical formulation, the concept of blank labels, the dynamic programming approach for loss computation, and basic decoding strategies.
- Deep Speech: Scaling up end-to-end speech recognition, Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, Andrew Y. Ng, 2014 arXiv preprint arXiv:1412.5567 DOI: 10.48550/arXiv.1412.5567 - This paper presents a practical application of CTC in a large-scale end-to-end speech recognition system, providing details on its implementation, training, and effective decoding strategies including beam search with language model integration.
- Speech and Language Processing (3rd ed. draft), Daniel Jurafsky and James H. Martin, 2025 (Online Draft) - An established textbook offering a pedagogical and comprehensive explanation of Connectionist Temporal Classification within the broader context of automatic speech recognition, suitable for advanced students seeking a structured overview.
- Supervised Sequence Labelling with Recurrent Neural Networks, Alex Graves, 2012 Vol. 385 (Springer) DOI: 10.1007/978-3-642-24797-2 - Based on the author's Ph.D. thesis, this book provides an in-depth theoretical explanation of Connectionist Temporal Classification, its underlying principles, and its connections to other sequence labeling techniques using recurrent neural networks.