Language Model Bootstrapping Using Neural Machine Translation for Conversational Speech Recognition
Table 1: Relative WERRs (%) with different post-editing techniques. Perplexity (PPL) is evaluated on a held-out in-domain dataset. Relative WERR captures the WER reduction w.r.t baseline trained on transcribed data only.
['[BOLD] Postprocessing', '[BOLD] Approach', '[BOLD] PPL', '[BOLD] Relative WERR %']
The approach of ingesting raw translations without any postprocessing yields a high perplexity translation component, resulting in negative WERR. We observe consistent improvements by introducing attention weight based post-editing. NE copy-over alone reduces the perplexity significantly. This, coupled with NE resampling and code mixing, results in a 5.83% WERR.