Neural Utterance Confidence Measure for RNN-Transducers and Two Pass Models
Ashutosh Gupta, Ankur Kumar, Dhananjaya Gowda, Kwangyoun Kim, Sachin Singh, Shatrughan Singh, Chanwoo Kim
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.
BibTeX
@inproceedings{icassp2021_neuralutterancec,
title = {Neural Utterance Confidence Measure for RNN-Transducers and Two Pass Models},
author = {Ashutosh Gupta and Ankur Kumar and Dhananjaya Gowda and Kwangyoun Kim and Sachin Singh and Shatrughan Singh and Chanwoo Kim},
booktitle = {ICASSP 2021},
year = {2021}
}