Acoustic and Word Lattice Based Algorithms for Confidence Scores

Word confidence scores are crucial for unsupervised learning in automatic speech recognition. In the last decade there has been a flourish of work on two fundamentally different approaches to compute confidence scores. The first paradigm is acoustic and the second is based on word lattices. The first approach is data-intensive and it requires to explicitly model the acoustic channel. The second approach is suitable for on-line (unsupervised) learning and requires no training. In this paper we present a comparative analysis of off-the-shelf and new algorithms for computing confidence scores, following the acoustic and lattice-based paradigms. We compare the performance of these algorithms across three tasks for small, medium and large vocabulary speech recognition tasks and for two languages (Italian and English). We show that word-lattice based algorithm provides consistent and effective performance across automatic speech recognition tasks