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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 Deep Dive

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

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

Advanced Speech Recognition and Synthesis

Prerequisites: Requires a strong foundation in machine learning, deep learning (specifically CNNs, RNNs, Transformers), and Python programming. Familiarity with audio signal processing concepts is beneficial.

Level: Advanced

What You'll Learn

Advanced ASR Architectures
Implement and analyze complex end-to-end ASR models like attention-based encoder-decoders and Transducers.
Speaker and Environment Adaptation
Apply techniques to adapt ASR models to different speakers, accents, and acoustic environments.
Advanced TTS Modeling
Construct sophisticated TTS models focusing on naturalness, prosody control, and voice cloning.
Neural Vocoders
Implement and evaluate modern neural vocoders for high-fidelity speech synthesis.
Model Optimization and Deployment
Apply techniques for optimizing ASR/TTS models for speed, size, and efficient deployment.
Evaluation Methodologies
Utilize advanced metrics and methodologies for evaluating the performance of ASR and TTS systems.

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