Relative Positional Encoding for Speech Recognition and Direct Translation
Table 2: ASR: Comparison on 2000h SWB+Fisher training set and Hub5’00 test sets. Absolute best is bolded, our best is italicized. WER↓ .
['[EMPTY]', '[BOLD] Models', '[BOLD] SWB', '[BOLD] CH']
The experiments on the larger dataset with 2000h follow the above results for 300h, continuing to show positive effects from that relative position encodings. Our best model is significantly better than previously published CTC [audhkhasi2018building] and LSTM-based [nguyen2019improving] models, and approaches the heavily tuned hybrid system [han2017capio] with dense TDNN-LSTM. It is likely possible to reach better error rates, with the help of ensembled models, further data augmentation, and language models. Our experiments here, however, show that the novel relative model is consistently better than the baseline, regardless of the data size and augmentation conditions.