4th International Conference on Spoken Language Processing
Philadelphia, PA, USA
In almost all applications of automatic speech recognition, especially in spontaneous speech tasks, the recognizer vocabulary cannot cover all occurring words. There is always a significant amount of out-of-vocabulary words even when the vocabulary size is very large. In this paper we present a new approach for the integration of out-of-vocabulary words into statistical language models. We use category information for all words in the training corpus to define a function that gives an approximation of the out-of-vocabulary word emission probability for each word category. This information is integrated into the language models. Although we use a simple acoustic model for out-of-vocabulary words, we achieve a 6% reduction of word error rate on spontaneous speech data with about 5% out-of-vocabulary rate.
Bibliographic reference. Gallwitz, Florian / Nöth, Elmar / Niemann, Heinrich (1996): "A category based approach for recognition of out-of-vocabulary words", In ICSLP-1996, 228-231.