Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard-300
Table 1: Effect of data preparation steps on WER [%] measured on Hub5’00. The second row corresponds to the Kaldi s5c recipe.
['filter frag.', 'filter noise', 'filter dup.', 'w/o LM swb', 'w/o LM chm', 'w/ LM swb', 'w/ LM chm']
We first investigate the importance of different data processing steps. The s5c Kaldi recipe includes a duplicate filtering step, in which the maximum number of occurrences of utterances with the same content is limited. We measure the impact of duplicate filtering and also the effect of filtering out word fragments and noise tokens from the training transcripts. Since the LM is trained on identically filtered transcripts from Fisher+Switchboard data, word fragment and noise token filters were applied consistently. Deactivating the duplicate filter is never harmful when an external LM is used, and the gains on CallHome can be substantial. Considering performance on the complete Hub5’00 data, the best systems either explicitly handle both word fragments and noise tokens or filter them all out. When an external LM is used, the best results are obtained when word fragment and noise token filters are activated and the duplicate filter is deactivated. This setting is also appealing in cases where the external LM may be trained on text data that will not contain word fragments or noise; thus, the remaining experiments are carried out with this system setting.