Method · The generator as the distilled artifact
Dataset-Distillation Generative Model for Speech Emotion Recognition
Fabian Ritter-Gutierrez, Kuan-Po Huang, Jeremy H. M Wong, Dianwen Ng, Hung-yi Lee, Nancy F. Chen, Eng Siong Chng
Interspeech 2024 · first public 2024-06-05 · arXiv 2406.02963
In one paragraph
Introduces the first dataset-distillation approach for speech, applied to Speech Emotion Recognition on IEMOCAP: a GAN is trained not to reconstruct real utterances but to distill discriminative information useful for downstream training, then replaces the dataset as a sampler of any desired synthetic size; reports comparable performance under the original class imbalance and a 0.3-point absolute UAR gain under balanced classes, with about 95% less storage and faster downstream training.
Where it sits
- The generator as the distilled artifact (Generative priors)
- Setting: Other data types
Design choices
| Prior / networks used | gan |
| What is stored | generator-weights |
Abstract (verbatim from arXiv)
Deep learning models for speech rely on large datasets, presenting computational challenges. Yet, performance hinges on training data size. Dataset Distillation (DD) aims to learn a smaller dataset without much performance degradation when training with it. DD has been investigated in computer vision but not yet in speech. This paper presents the first approach for DD to speech targeting Speech Emotion Recognition on IEMOCAP. We employ Generative Adversarial Networks (GANs) not to mimic real data but to distil key discriminative information of IEMOCAP that is useful for downstream training. The GAN then replaces the original dataset and can sample custom synthetic dataset sizes. It performs comparably when following the original class imbalance but improves performance by 0.3% absolute UAR with balanced classes. It also reduces dataset storage and accelerates downstream training by 95% in both cases and reduces speaker information which could help for a privacy application.
BibTeX (generated; prefer the venue's official entry)
@article{rittergutierrez2024dataset,
title = {Dataset-Distillation Generative Model for Speech Emotion Recognition},
author = {Fabian Ritter-Gutierrez and Kuan-Po Huang and Jeremy H. M Wong and Dianwen Ng and Hung-yi Lee and Nancy F. Chen and Eng Siong Chng},
journal = {Interspeech 2024},
year = {2024}
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