A multi-class approach for modelling out-of-vocabulary words

In this paper we present a multi-class extension to our approach for modelling out-of-vocabulary (OOV) words [1]. Instead of augmenting the word search space with a single OOV model, we add several OOV models, one for each class of words. We present two approaches for designing the OOV word classes. The first approach relies on using common part-of-speech tags. The second approach is a data-driven two-step clustering procedure, where the first step uses agglomerative clustering to derive an initial class assignment, while the second step uses iterative clustering to move words from one class to another in order to reduce the model perplexity. We present experiments within the JUPITER weather information domain. Results show that the multi-class model significantly improves performance over using a single OOV class. For an OOV detection rate of 70%, the false alarm rate is reduced from 5.3% for a single class to 2.9% for an eight-class model.