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
Multi-Lingual and Cross-Lingual ASR
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- Massively Multilingual Speech Recognition with a Single Transformer, Yu-An Chung, Wei-Ning Hsu, Alexis Conneau, and James Glass, 2020 Interspeech 2020 (International Speech Communication Association (ISCA)) DOI: 10.21437/Interspeech.2020-2212 - A research paper demonstrating the effectiveness of a single Transformer model for multi-lingual ASR, covering shared architectures and data pooling concepts.
- wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations, Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, and Michael Auli, 2020 Advances in Neural Information Processing Systems (NeurIPS), Vol. 33 DOI: 10.48550/arXiv.2006.11477 - This paper introduces a widely adopted self-supervised pre-training method that is foundational for modern cross-lingual ASR through fine-tuning on limited target language data.
- Unsupervised Cross-Lingual Representation Learning for Speech Recognition, Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli, 2020 arXiv preprint arXiv:2006.13979 DOI: 10.48550/arXiv.2006.13979 - Extends the wav2vec 2.0 framework specifically for unsupervised cross-lingual speech representation learning, offering significant improvements for low-resource ASR.
- Speech and Language Processing (3rd ed. draft), Daniel Jurafsky and James H. Martin, 2025 - A comprehensive textbook providing a thorough introduction to speech recognition principles, encompassing traditional methods and recent advancements in deep learning. Relevant chapters discuss language modeling and adaptation.