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Energy-based language models (ELMs) parameterize an unnormalized distributionfor natural sentences and are radically different from popular autoregressivelanguage models (ALMs). As an important application, ELMs have beensuccessfully used as a means for calculating sentence scores in speechrecognition, but they all use less-modern CNN or LSTM networks. The recentprogress in Transformer networks and large pretrained models such as BERT andGPT2 opens new possibility to further advancing ELMs. In this paper, we exploredifferent architectures of energy functions and different training methods toinvestigate the capabilities of ELMs in rescoring for speech recognition, allusing large pretrained models as backbones.

Exploring Energy-based Language Models with Different Architectures and Training Methods for Speech Recognition