Corpus-Based Hidden Markov Modelling of the Fundamental Frequency of Lithuanian

Abstract

This paper presents the corpus-driven approach in building the computational model of fundamental frequency, or ${\mathit{F}}_0$ , for Lithuanian language. The model was obtained by training the HMM-based speech synthesis system HTS on six hours of speech coming from multiple speakers. Several gender specific models, using different parameters and different contextual factors, were investigated. The models were evaluated by synthesizing ${\mathit{F}}_0$ contours and by comparing them to the original ${\mathit{F}}_0$ contours using criteria of root mean square error (RMSE) and voicing classification error. The HMM-based models showed an improvement of the RMSE over the mean-based model that predicted ${\mathit{F}}_0$ of the vowel on the basis of its average normalized pitch.