A comparative study on various confidence measures in large vocabulary speech recognition

Abstract

In this paper, we have conducted a comparative study on several confidence measures (CM) for large vocabulary speech recognition. Firstly, we propose a novel high-level CM that is based on the inter-word mutual information (MI). Secondly, we experimentally investigate several popular low-level CM, such as word posterior probabilities, N-best counting, likelihood ratio testing (LRT), etc. Finally, we have studied a simple linear interpolation strategy to combine the best low-level CM with the best high-level CM. All of these CM are examined in two large vocabulary ASR tasks, namely the Switchboard task and a Mandarin dictation task, to verify the recognition errors in baseline recognition systems. Experimental results show: (1) the proposed MI-based CM greatly surpass another existing high-level CM which are based on the LSA technique; (2) among all low-level CM, word posteriori probabilities give the best verification performance; (3) when combining the word posteriori probabilities with the MI-based CM, the equal error rate is reduced from 24.4% to 23.9% in the Switchboard task and from 17.5% to 16.2% in the Mandarin dictation task.