Confidence Measures for Evaluating Pronunciation Models
Gethin Williams, Steve J. Renals · ERA · 1998
In this paper, we investigate the use of confidence measures for the evaluation of pronunciation models and the employment of these evaluations in an automatic baseform learning process. The confidence measures and pronunciation models are obtained from the ABBOT hybrid Hidden Markov Model/Artificial Neural Network (HMM/ANN) Large Vocabulary Continuous Speech Recognition (LVCSR) system [8]. Experiments were carried out for a number of baseform learning schemes using the ARPA North American Business News (NAB) and the Broadcast News (BN) corpora from which it was found that a confidence measure based scheme provided the largest reduction in Word Error Rate (WER). 2. INTRODUCTION A confidence measure may be defined as a function which quantifies how well a model matches some acoustic data, where the values of the function must be comparable across utterances. More specifically, an acoustic confidence measure is one which is derived exclusively from an acoustic model. As an acoustic con...