Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization
CoRR(2024)
摘要
In this paper, we present a novel bilevel optimization-based training
approach to training acoustic models for automatic speech recognition (ASR)
tasks that we term bi-level joint unsupervised and supervised training
(BL-JUST). BL-JUST employs a lower and upper level optimization with an
unsupervised loss and a supervised loss respectively, leveraging recent
advances in penalty-based bilevel optimization to solve this challenging ASR
problem with affordable complexity and rigorous convergence guarantees. To
evaluate BL-JUST, extensive experiments on the LibriSpeech and TED-LIUM v2
datasets have been conducted. BL-JUST achieves superior performance over the
commonly used pre-training followed by fine-tuning strategy.
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关键词
bilevel optimization,automatic speech recognition,deep neural networks,unsupervised training,supervised training
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