Language Model Bootstrapping Using Neural Machine Translation for Conversational Speech Recognition
Table 5: Relative WERRs (%) with varying levels of in-domain transcribed data.
['[BOLD] Transcribed', '[BOLD] WERR %']
We now attempt to address the following question: what are the relative gains provided by the translation data during different phases of bootstrapping? In particular, we measure the WERR between the baseline and translation-augmented LMs, by varying the in-domain transcribed utterances from 10K to 200K. We observe that the combined WERR after post-editing and adaptation increases from 7.86% to 15.65% as the amount of in-domain data reduces. Note that in this experiment, we use the same AM trained on 180 hours data, in order to precisely study the effect of data augmentation for LM. The WERR we report is hence an underestimate, and will probably be much higher, if the AM was trained using similar levels of transcribed data.