Neural Utterance Confidence Measure for RNN-Transducers and Two Pass Models.
ICASSP(2021)
Abstract
In this paper, we propose methods to compute confidence score on the predictions made by an end-to-end speech recognition model in a 2-pass framework. We use RNN-Transducer for a streaming model, and an attention-based decoder for the second pass model. We use neural technique to compute the confidence score, and experiment with various combinations of features from RNN-Transducer and second pass models. The neural confidence score model is trained as a binary classification task to accept or reject a prediction made by speech recognition model. The model is evaluated in a distributed speech recognition environment, and performs significantly better when features from second pass model are used as compared to the features from streaming model.
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Key words
Neural confidence measure,end-to-end speech recognition,RNN-Transducers,Two pass
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