This paper describes an approach to detect out-of-vocabulary words in spontaneous speech using a language model built on semantic categories and a new type of generalized word models consisting of a mixture of specific and general acoustic units. We demonstrate the construction of the generalized word models as replacements for surnames in a German spontaneous travel planning task GSST. We show that the use of our generalized word models improves recognition accuracy in cases where out-of-vocabulary words appear and does not lead to a degradation of the overall recognition accuracy. In our experiments we measured recall and precision rates of OOV-detection which are close to their theoretic optimum. Furthermore, we compared the effect of using cross-word-triphones vs. using context-independent cross-word models. We show that when using generalized word models with cross-word-triphones, the expected number of consequential errors following an OOV word can be reduced significantly by 37%.
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