Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T LossThis is the final version of the paper submitted to the ICASSP 2020 on Oct 21, 2019.
Table 4: Limited right context per layer for audio encoder.
['Audio Mask left', 'Audio Mask right', 'Label Mask left', 'WER (%) Test-clean', 'WER (%) Test-other']
Similarly, we explored the use of limited right context to allow the model to see some future audio frames, in the hope of bridging the gap between a streamable T-T model (left = 10, right = 0) and a full attention T-T model (left = 512, right = 512). Since we apply the same mask for every layer, the latency introduced by using right context is aggregated over all the layers. To explore the right context impact for modeling, we did comparisons with fixed 512 frames left context per layer to compared with full attention T-T model. Compared with streamable T-T model, 2 frames right context per layer (around 1 sec of latency) brings around 30% improvements.