Optimal transport meets speech: a tutorial review

arXiv:2609.31787 · eess.AS, cs.AI, cs.CL, cs.LG · Submitted 2026-09-24 · Read on arXiv

eess.AS, cs.AI, cs.CL, cs.LG

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Adaptation — 56× in this paper · explained in 1 episode
• Optimal Transport — 55× in this paper · explained in 5 episodes
• Alignment — 45× in this paper · explained in 1 episode
• Domain Adaptation — 38× in this paper · explained in 5 episodes
• Wasserstein Distance — 30× in this paper · explained in 4 episodes
• Transfer Learning — 17× in this paper · explained in 10 episodes
• Flow Matching — 14× in this paper · explained in 7 episodes
• Generative Modeling — 14× in this paper · explained in 3 episodes
• Regularization — 14× in this paper · explained in 3 episodes
• Gradient Flow — 11× in this paper · explained in 2 episodes
• Diffusion Model — 9× in this paper · explained in 8 episodes
• Loss Function — 9× in this paper · explained in 2 episodes
• Generative Model — 8× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Normalizing Flow — 7× in this paper · explained in 3 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Representation Learning — 6× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 5× in this paper
• Latent Space — 5× in this paper · explained in 7 episodes
• Sparsity — 5× in this paper · explained in 4 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Score Function — 4× in this paper · explained in 4 episodes
• Adversarial Training — 3× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Generalization — 3× in this paper · explained in 13 episodes
• Input Convex Neural Network — 3× in this paper
• Modality Gap — 3× in this paper · explained in 4 episodes
• Normalizing Flows — 3× in this paper · explained in 4 episodes
• Objective Function — 3× in this paper · explained in 2 episodes
• Optimal Transport (OT) — 3× in this paper · explained in 4 episodes
• Rectified Flow — 3× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Verification — 3× in this paper · explained in 2 episodes
• ACM Transactions on Graphics — 2× in this paper
• Adaptability — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Diffusion Probabilistic Model — 2× in this paper
• Diffusion Processes — 2× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• Mean Absolute Error — 2× in this paper
• Multimodal Learning — 2× in this paper · explained in 2 episodes
• Phone-Fortified Perceptual Loss — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Self-Supervised Learning — 2× in this paper · explained in 7 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 1× in this paper
• Conditional Flow Matching — 1× in this paper
• Connectionist Temporal Classification — 1× in this paper
• Conservation Laws — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Data Fusion — 1× in this paper · explained in 3 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Dynamic Modeling — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Rectified Flow Matching — 1× in this paper
• Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
• Stochastic Dynamics — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Improving Sequence-to-Sequence Learning via Optimal Transport
• Neural Ordinary Differential Equations
• Scaling Algorithms for Unbalanced Transport Problems
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Distilling the Knowledge of BERT for CTC-based ASR
• Conformer: Convolution-augmented Transformer for Speech Recognition
• Sequence Transduction with Recurrent Neural Networks
• Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical Distillation
• Distilling the Knowledge in a Neural Network
• An Introduction to Flow Matching and Diffusion Models
• Linguistic Knowledge Transfer Learning for Speech Enhancement
• Auto-Encoding Variational Bayes
• Transport-based analysis, modeling, and learning from signal and data distributions
• Neural Optimal Transport
• Pre-training for Speech Translation: CTC Meets Optimal Transport
• Bridging The Multi-Modality Gaps of Audio, Visual and Linguistic for Speech Enhancement
• Flow Matching for Generative Modeling
• Flow Matching Guide and Code
• Optimal Transport for Machine Learners
• Generative Modeling by Estimating Gradients of the Data Distribution

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