Personalization of CTC Speech Recognition Models using Contextual Adapters and Adaptive Boosting. (arXiv:2210.09510v2 [cs.CL] UPDATED)

End-to-end speech recognition models trained using joint Connectionist
Temporal Classification (CTC)-Attention loss have gained popularity recently.
In these models, a non-autoregressive CTC decoder is often used at inference
time due to its speed and simplicity. However, such models are hard to
personalize because of their conditional independence assumption that prevents
output tokens from previous time steps to influence future predictions. To
tackle this, we propose a novel two-way approach that first biases the encoder
with attention over a predefined list …

arxiv boosting personalization speech speech recognition speech recognition models