Estimates of confidence for the output of a speech recognition system can be used in many practical applications of speech recognition technology. They can be employed for detecting possible errors and can help to avoid undesirable verification turns in automatic inquiry systems. In this paper we propose to estimate the confidence in a hypothesized word as its posterior probability, given all acoustic feature vectors of the speaker utterance. The basic idea of our approach is to estimate the posterior word probabilities as the sum of all word hypothesis probabilities which represent the occurrence of the same word in more or less the same segment of time. The word hypothesis probabilities are approximated by paths in a wordgraph and are computed using a simplified forward-backward algorithm. We present experimental results on the NORTH AMERICAN BUSINESS (NAB'94) and the German VERBMOBIL recognition task. 1. INTRODUCTION With the rising number of different application areas for speech ...

In this paper we present and compare several confidence measures for large vocabulary continuous speech recognition. We show that posterior word probabilities computed on word graphs and N-best lists clearly outperform non-probabilistic confidence measures, e.g. the acoustic stability and the hypothesis density. In addition, we prove that the estimation of posterior word probabilities on word graphs yields better results than their estimation on N-best lists and discuss both methods in detail. We present experimental results on three different corpora, the English NAB '94 20k development corpus, the German VERBMOBIL '96 evaluation corpus and a Dutch corpus, which has been recorded with a train timetable information system in the ARISE project. 1. INTRODUCTION In previous studies, the combination of several confidence features was investigated. These features were collected during the acoustic decoding process, e.g. [1] or were extracted from Nbest lists and word graphs, e.g. [2, 5]. ...