Mixup is a popular regularization technique for training deep neural networks that improves generalization and increases robustness to certain distribution shifts. It perturbs input training data in the direction of other randomly-chosen instances in the training set. To better leverage the structure of the data, we extend mixup in a simple, broadly applicable way to k-mixup, which perturbs k-batches of training points in the direction of other k-batches. The perturbation is done with displacement interpolation, i.e. interpolation under the Wasserstein metric. We demonstrate theoretically and in simulations that k-mixup preserves cluster and manifold structures, and we extend theory studying the efficacy of standard mixup to the k-mixup case. Our empirical results show that training with k-mixup further improves generalization and robustness across several network architectures and benchmark datasets of differing modalities. For the wide variety of real datasets considered, the performance gains of k-mixup over standard mixup are similar to or larger than the gains of mixup itself over standard ERM after hyperparameter optimization. In several instances, in fact, k-mixup achieves gains in settings where standard mixup has negligible to zero improvement over ERM.
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MixRep: Hidden Representation Mixup for Low-Resource Speech Recognition
In this paper, we present MixRep, a simple and effective data augmentation strategy based on mixup for low-resource ASR. MixRep interpolates the feature dimensions of hidden representations in the neural network that can be applied to both the acoustic feature input and the output of each layer, which generalizes the previous MixSpeech method. Further, we propose to combine the mixup with a regularization along the time axis of the input, which is shown as complementary. We apply MixRep to a Conformer encoder of an E2E LAS architecture trained with a joint CTC loss. We experiment on the WSJ dataset and subsets of the SWB dataset, covering reading and telephony conversational speech. Experimental results show that MixRep consistently outperforms other regularization methods for low-resource ASR. Compared to a strong SpecAugment baseline, MixRep achieves a +6.5% and a +6.7% relative WER reduction on the eval92 set and the Callhome part of the eval'2000 set.
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- Award ID(s):
- 2234916
- PAR ID:
- 10532339
- Editor(s):
- ISCA
- Publisher / Repository:
- ISCA
- Date Published:
- Journal Name:
- Interspeech
- Edition / Version:
- 1
- Volume:
- 1
- Issue:
- 1
- ISSN:
- 2958-1796
- Page Range / eLocation ID:
- 1304 to 1308
- Subject(s) / Keyword(s):
- End-to-end Speech Recognition Low-resource Mixup Hidden Representations Data Augmentation Child Adult speech recognition
- Format(s):
- Medium: X Size: 580KB
- Size(s):
- 580KB
- Location:
- Univ. of Texas at Dallas
- Sponsoring Org:
- National Science Foundation
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