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Automatic Speech Recognition (ASR) systems have been gaining popularity inthe recent years for their widespread usage in smart phones and speakers.Building ASR systems for task-specific scenarios is subject to the availabilityof utterances that adhere to the style of the task as well as the language inquestion. In our work, we target such a scenario wherein task-specific textdata is available in a language that is different from the target language inwhich an ASR Language Model (LM) is expected. We use Neural Machine Translation(NMT) as an intermediate step to first obtain translations of the task-specifictext data. We then train LMs on the 1-best and N-best translations and studyways to improve on such a baseline LM. We develop a procedure to derive wordconfusion networks from NMT beam search graphs and evaluate LMs trained onthese confusion networks. With experiments on the WMT20 chat translation taskdataset, we demonstrate that NMT confusion networks can help to reduce theperplexity of both n-gram and recurrent neural network LMs compared to thosetrained only on N-best translations.

Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation