| Paper: | SP-P11.3 | ||
| Session: | Topics in Large Vocabulary Continuous Speech Recognition | ||
| Time: | Thursday, May 20, 09:30 - 11:30 | ||
| Presentation: | Poster | ||
| Topic: | Speech Processing: Confidence Measures/Rejection | ||
| Title: | HYBRID LANGUAGE MODELS FOR OUT OF VOCABULARY WORD DETECTION IN LARGE VOCABULARY CONVERSATIONAL SPEECH RECOGNITION | ||
| Authors: | Ali Yazgan; Johns Hopkins University | ||
| Murat Saraclar; AT&T Labs - Research | |||
| Abstract: | In this paper, we propose a method for out-of-vocabulary (OOV) word detection and taking a step toward open vocabulary automatic speech recognition. The proposed method uses a hybrid language model combining words and sub-word units such as phones or syllables. We describe a detection algorithm based on the posterior count of the OOV words given the hybrid model, and compare it to using the posterior probability of the best word string given a conventional word only model. Experimental results on the Switchboard corpus are presented for different vocabulary sizes. The new method yields a gain of over 10% in OOV word detection. In addition, a modest number of the OOV word pronunciations are found correctly. | ||
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