  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.
