Classifier design for verification of multi-class recognition decision

This paper investigates a 2-class classifier approach with the aim of improving the word verification performance. The classifier operates on a discriminant function which is a linear combination of the smoothed likelihood ratios for the N-best candidates and the background (BG) and out-of-vocabulary (OOV) filler models, and is optimized using discriminative training to minimize the classification error. This paper discusses several strategies involving the likelihood ratio based formulation and the use of N-best candidates and the BG and OOV models in the classifier. In word verification experiments using a connected-digit database containing utterances recorded in a moving car with a hands-free microphone, the likelihood ratio based formulation achieved a relative error reduction of 35% in comparison with a likelihood based formulation. In addition, we observed that the use of N-best candidates and the BG and OOV models improved the performance with a relative error reduction of roughly 10%.