Semi-Supervised Discriminative Language Modeling with Out-of-Domain Text Data
Arda Celebi and Murat Saraclar
One way to improve the accuracy of automatic speech recognition (ASR) is to use
discriminative language modeling (DLM), which enhances discrimination by
learning where the ASR hypotheses deviate from the uttered sentences. However,
DLM requires large amounts of ASR output to train. Instead, we can simulate the
output of an ASR system, in which case the training becomes semi-supervised.
The advantage of using simulated hypotheses is that we can generate as many
hypotheses as we want provided that we have enough text material. In typical
scenarios, transcribed in-domain data is limited but large amounts of
out-of-domain (OOD) data is available. In this study, we investigate how
semi-supervised training performs with OOD data. We find out that OOD data can
yield improvements comparable to in-domain data.
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