About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Conference paper
Confidence measure driven scalable two-pass recognition strategy for large list grammars
Abstract
In this article we will discuss recognition performance on large list grammars, a class of tasks often encountered in telephony applications. In these tasks, the user makes a selection from a large list of choices (e.g. stock quotes, yellow pages, etc). Though the redundancy of the complete utterance is often high enough to achieve high recognition accuracy, large search space presents a challenge for the recognizer, in particular, when realtime, low latency performance is required. We propose a confidence measure driven two-pass search strategy, exploiting the high mutual information between grammar states to improve pruning efficiency while minimizing the need for memory.