| Paper: | SP-P11.12 | ||
| 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: | FILLER MODEL BASED CONFIDENCE MEASURES FOR SPOKEN DIALOGUE SYSTEMS: A CASE STUDY FOR TURKISH | ||
| Authors: | Aydin Akyol; Sabancı University | ||
| Hakan Erdogan; Sabancı University | |||
| Abstract: | Because of the inadequate performance of speech recognition systems, an accurate confidence scoring mechanism should be employed to understand the user requests correctly. To determine a confidence score for a hypothesis, certain confidence features are combined. In this work, the performance of filler-model based confidence features have been investigated. Five types of filler model networks were defined: triphone-network, phone-network, phone-class network, 5-state catch-all model and 3-state catch-all model. First all models were evaluated in a Turkish speech recognition task in terms of their ability to correctly tag (recognition-error or correct) recognition hypotheses. Here, the best performance was obtained from triphone recognition network. Then the performance of reliable combinations of these models were investigated and it was observed that certain combinations of filler models could significantly improve the accuracy of the confidence annotation. | ||
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