Language Model Bootstrapping Using Neural Machine Translation for Conversational Speech Recognition
Table 3: Relative WERR % by interaction scenarios captured along with the extent of coverage in transcribed collections. Named entity proportion in the utterances is given by NE %. Adaptation contribution % captures the relative contribution of adaptation towards WERR. For e.g., with a 5.75% post-editing WERR, adaptation yields an additional 1.51% WERR towards a combined WERR of 7.26%, i.e. 20.80%.
['[BOLD] Coverage', '[BOLD] Interaction', '[BOLD] Post-editing', '[BOLD] Combined', '[BOLD] Adaptation', '[BOLD] NE%']
Cleo prompts cover multiple interaction use cases. In order to derive fine grained insights into the effect of translations, we study WERR on test utterances manually categorized into scenarios. In order to isolate the gains obtained from post-editing and adaptation, we study both the post-editing and combined WERR. We also analyse the proportion of named entities for utterances belonging to each of these scenarios. Following observations can be made from this analysis.