ASR error detection and recognition rate estimation using deep bidirectional recurrent neural networks

Abstract

Recurrent neural networks (RNNs) have recently been applied as the classifiers for sequential labeling problems. In this paper, deep bidirectional RNNs (DBRNNs) are applied for the first time to error detection in automatic speech recognition (ASR), which is a sequential labeling problem. We investigate three types of ASR error detection tasks, i.e. confidence estimation, out-of-vocabulary word detection and error type classification. We also estimate recognition rates from the error type classification results. Experimental results show that the DBRNNs greatly outperform conditional random fields (CRFs), especially for the detection of infrequent error labels. The DBRNNs also slightly outperform the CRFs in recognition rate estimation. In addition, experiments using a reduced size of training data suggest that the DBRNNs have a better generalization ability than the CRFs owing to their word vector representation in a low-dimensional continuous space. As a result, the DBRNNs trained using only 20% of the training data show higher error detection performance than the CRFs trained using the full training data.