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Adaptive Endpointing with Deep Contextual Multi-armed Bandits
Current endpointing (EP) solutions learn in a supervised framework, which
does not allow the model to incorporate feedback and improve in an online
setting. Also, it is a common practice to utilize costly grid-search to find
the best configuration for an endpointing model. In this paper, we aim to
provide a solution for adaptive endpointing by proposing an efficient method
for choosing an optimal endpointing configuration given utterance-level audio
features in an online setting, while avoiding hyperparameter grid-search. Our
method does not require ground truth labels, and only uses online learning from
reward signals without requiring annotated labels. Specifically, we propose a
deep contextual multi-armed bandit-based approach, which combines the
representational power of neural networks with the action exploration behavior
of Thompson modeling algorithms. We compare our approach to several baselines,
and show that our deep bandit models also succeed in reducing early cutoff
errors while maintaining low latency
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