Error type classification and word accuracy estimation using alignment features from word confusion network

Abstract

This paper addresses error type classification in continuous speech recognition (CSR). In CSR, errors are classified into three types, namely, the substitution, insertion and deletion errors, by making an alignment between a recognized word sequence and its reference transcription with a dynamic programming (DP) procedure. We propose a method for deriving such alignment features from a word confusion network (WCN) without using the reference transcription. We show experimentally that the WCN-based alignment features steadily improve the performance of error type classification. They also improve the performance of out-of-vocabulary (OOV) word detection, since OOV word utterances are highly correlated with a particular alignment pattern. In addition, we show that the word accuracy can be estimated from the WCN-based alignment features and more accurately from the error type classification result without using the reference transcription.