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CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders

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Heng-Jui Chang, Ning Dong, Ruslan Mavlyutov, Sravya Popuri, Yu-An Chung

Computation and Language · Submitted 14 Sep 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim CoLLD outperforms prior methods and closes the gap between small and large models on multilingual speech-to-text translation and recognition benchmarks.

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arXiv ID2309.07707 (version 2)
Pith IDN7XBGEOH, Pith's permanent record of this paper and its review
CitationHeng-Jui Chang, Ning Dong, Ruslan Mavlyutov, Sravya Popuri, Yu-An Chung. "CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders." arXiv:2309.07707 (2023). https://pith.science/paper/N7XBGEOH
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What the abstract itself states, as probabilities: claims first 0.10 · states limitation 0.07 · human participants 0.05 · controlled comparison 0.61 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.96 · mentions code or data availability 0.02.

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arXiv listing abstract v2. Results and the main claim are underlined; .

Large-scale self-supervised pre-trained speech encoders outperform conventional approaches in speech recognition and translation tasks. Due to the high cost of developing these large models, building new encoders for new tasks and deploying them to on-device applications are infeasible. Prior studies propose model compression methods to address this issue, but those works focus on smaller models and less realistic tasks. Thus, we propose Contrastive Layer-to-layer Distillation (CoLLD), a novel knowledge distillation method to compress pre-trained speech encoders by leveraging masked prediction and contrastive learning to train student models to copy the behavior of a large teacher model. CoLLD outperforms prior methods and closes the gap between small and large models on multilingual speech-to-text translation and recognition benchmarks.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Large-scale self-supervised pre-trained speech encoders outperform conventional approaches in speech recognition and translation tasks. background 1.00 0.09 0.03 asserted 1.00
s1 Due to the high cost of developing these large models, building new encoders for new tasks and deploying them to on-device applications are infeasible. background 0.93 0.09 0.02 asserted 1.00
s2 Prior studies propose model compression methods to address this issue, but those works focus on smaller models and less realistic tasks. gap 0.88 0.05 0.02 asserted 1.00
s3 Thus, we propose Contrastive Layer-to-layer Distillation (CoLLD), a novel knowledge distillation method to compress pre-trained speech encoders by leveraging masked prediction and contrastive learning to train student models to copy the behavior of a large teacher model. contribution 0.63 0.86 0.11 asserted 0.99
s4 CoLLD outperforms prior methods and closes the gap between small and large models on multilingual speech-to-text translation and recognition benchmarks. result 1.00 0.92 0.91 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.95.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Large-scale self-supervised pre-trained speech encoders outperform conventional approaches in speech recognition and translation tasks. Due to the high cost of developing these large models, building new encoders for new tasks and deploying them to on-device applications are infeasible. Prior studies propose model compression methods to address this issue, but those works focus on smaller models and less realistic tasks. Thus, we propose Contrastive Layer-to-layer Distillation (CoLLD), a novel knowledge distillation method to compress pre-trained speech encoders by leveraging masked prediction and contrastive learning to train student models to copy the behavior of a large teacher model. CoLLD outperforms prior methods and closes the gap between small and large models on multilingual speech-to-text translation and recognition benchmarks.

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