Language Model Bootstrapping Using Neural Machine Translation for Conversational Speech Recognition
Table 2: Relative WERRs (%) with different NMT adaptation strategies. Note that these results include the effect of NE resampling and code mixing techniques.
['[BOLD] Adaptation', '[BOLD] Approach', '[BOLD] PPL', '[BOLD] Relative WERR %']
For MT training data selection, we retain only top-25% (out of 8.4M) sentences w.r.t. their relative similarity with in-domain data. This reduction in training data impacts the BLEU score adversely. Amongst the sentence representation techniques, LASER and SIF embeddings outperform the unweighted averaging approach in terms of BLEU score. Interestingly, while the unweighted averaging achieves lowest perplexity on held-out in-domain dataset, the gains don’t carry over while measuring overall ASR performance. SIF embedding based selection achieves the highest WERR of 7.23%, followed closely by LASER encoder representation.