Hypothesis dependent threshold setting for improved out-of-vocabulary data rejection

An efficient rejection procedure is necessary to reject out-of-vocabulary words and noise tokens that occur in voice activated vocal services. Garbage or filler models are very useful for such a task. However, a post-processing of the recognized hypothesis, based on a likelihood ratio statistic test, can refine the decision and improve performance. These tests can be applied either on acoustic parameters or on phonetic or prosodic parameters that are not taken into account by the HMM-based decoder. This paper focuses on the post-processing procedure and shows that making the likelihood ratio decision threshold dependent on the recognized hypothesis largely improves the efficiency of the rejection procedure. Models and anti-models are one of the key-points of such an approach. Their training and usage are also discussed, as well as the contextual modeling involved. Finally results are reported on a field database collected from a 2000-word directory task using various phonetic and prosodic parameters