Phrase-Level Class based Language Model for Mandarin Smart Speaker Query Recognition
Table 1: Consistency results
['[EMPTY]', '[BOLD] Baseline', '[BOLD] rescore(sub)', '[BOLD] rescore(root+sub)']
Column 2 indicates the results where only the sub-grammars are pruned, while column 3 shows the results when all grammars are pruned. Results show that our method achieves the same accuracy as that as baseline on the test set ‘spearker_201812’ and even better results in ‘speaker_201901’. A reasonable explanation for this is that the on-the-fly approach has a much smaller decoder graph compared with the baseline, so it might be more straightforward to reach the correct LM score, and the unreliable decoding path can be cut more efficiently, leading to better results.