An effective acoustic modeling of names based on model induction
Taeyoon Kim, Sun-Mee Kang, Hanseok Ko · 2002
In a speech recognition based automatic directory assistance service, name modeling is an important issue that directly affects the overall system performance. In this paper, we propose an effective name modeling method considering the similarity property of names. In particular, we use explicit models to capture the common surnames (as in Korean names) while phone models are used to capture the less common first names. The proposed algorithm includes the surname model induction as a remedy to the insufficient training data problem caused by the model number increase. To efficiently induce the surname model, a model selection method based on the Bayesian information criterion (BIC) is introduced. Our experiment shows that the proposed name modeling method is effective and that the model induction method using BIC produces compact but accurate models.