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
Diffusion Models for Vocoding
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- Denoising Diffusion Probabilistic Models, Jonathan Ho, Ajay Jain, Pieter Abbeel, 2020 Advances in Neural Information Processing Systems 33, Vol. 33 (Curran Associates, Inc.) DOI: 10.5555/3455702.3455871 - This seminal paper introduced the Denoising Diffusion Probabilistic Models (DDPM) framework, detailing the forward and reverse processes, and the simplified training objective, which established the foundation for diffusion models.
- Denoising Diffusion Implicit Models, Jiaming Song, Chenlin Meng, Stefano Ermon, 2021 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2010.02502 - This paper introduced Denoising Diffusion Implicit Models (DDIM), presenting a method for significantly faster inference with fewer steps while maintaining generation quality, a critical contribution for applications requiring efficient sampling.
- DiffWave: A Versatile Diffusion Model for Audio Synthesis, Zhifeng Kong, Wei Ping, Kaiming Ren, Kexin Ren, and Qifeng Liu, 2021 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2009.09761 - This work was among the first to successfully apply diffusion models to high-fidelity audio waveform generation, demonstrating its promise for vocoding and general audio synthesis by adapting the DDPM framework for 1D audio signals.
- ProDiff: Progressive Fast Diffusion Model for High-Quality Text-to-Speech, Rongjie Huang, Zhou Zhao, Huadai Liu, Jinglin Liu, Chenye Cui, Yi Ren, 2022 Proceedings of the 30th ACM International Conference on Multimedia (ACM) DOI: 10.48550/arXiv.2207.05831 - This research proposed ProDiff, an approach for high-quality text-to-speech that employs a diffusion model for the vocoding component and incorporates methods for accelerating inference, addressing a key challenge of diffusion models in speech synthesis.