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
Generative Adversarial Networks (GANs) in TTS
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- Generative Adversarial Networks, Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, 2014 arXiv preprint arXiv:1406.2661 DOI: 10.48550/arXiv.1406.2661 - Introduces the fundamental concept and framework of Generative Adversarial Networks, including the min-max objective function.
- HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis, Jungil Kong, Jaehyeon Kim, Jaekyoung Bae, 2020 Advances in Neural Information Processing Systems, Vol. 33 (NeurIPS) - Presents a highly efficient and high-fidelity GAN-based vocoder, which became a standard for generating raw audio from acoustic features in TTS.
- MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis, Kundan Kumar, Rithesh Kumar, Alain de Boissiere, Lucas Gestin, Flavien Marion, Rony Rodriguez, Jean-Baptiste Roux, Sampath Singam, Aaron van den Oord, S. Zeghidour, Rafael Soricut, 2019 Advances in Neural Information Processing Systems, Vol. 32 (NeurIPS) DOI: 10.55916/HV8C-T992 - Introduces an early and influential GAN-based model for generating high-quality speech waveforms conditioned on mel-spectrograms.
- Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech, Jaehyeon Kim, Jungil Kong, Inwan Yoon, 2021 International Conference on Machine Learning (ICML), Vol. 139 (PMLR (Proceedings of Machine Learning Research)) DOI: 10.48550/PMLR.v139.kim21h - Presents an end-to-end TTS model that leverages a conditional VAE and adversarial training for both acoustic feature and waveform generation, achieving high quality and efficiency.