Self-Attention Transducers for End-to-End Speech Recognition
Table 3: Comparison of SA-T and other models.
['[BOLD] Model', '[BOLD] Dev', '[BOLD] Test']
Compared to the baseline RNN-T, the SA-T achieves 8.2% and 9.5% relative CER reduction on dev and test set respectively. And it also outperforms the attention-based LAS model reported in [changhaoshan2019]. We suppose that more trainable parameters, deeper network structure and more effective method to model long-term dependencies make the SA-T obtain a good performance. Furthermore, we notice that the SA-T with path-aware regularization can achieve a 21.3% relative CER reduction over the baseline RNN-T.