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"In several ASR use cases  training and adaptation of domain-specific LMs can only rely on a small amount of manually verified text transcriptions and sometimes a limited amount of in-domain speech  Training of LSTM LMs in such limited data scenarios can benefit from alternate uncertain ASR hypotheses  as observed in our recent work  In this paper  we propose a method to train Transformer LMs on ASR confusion networks  We evaluate whether these self-attention based LMs are better at exploiting alternate ASR hypotheses as compared to LSTM LMs  Evaluation results show that Transformer LMs achieve 3-6  relative reduction in perplexity on the AMI scenario meetings but perform similar to LSTM LMs on the smaller Verbmobil conversational corpus "
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