Development of a hidden Markov model (HMM)-based Korean speech synthesis system and its evaluation is described. Statistical HMM models for Korean speech units are trained with the hand-labeled speech database including the contextual information about phoneme, morpheme, word phrase, utterance, and break strength. The developed system produced speech with a fairly good prosody. The synthesized speech is evaluated and compared with that of our corpus-based unit concatenating Korean text-to-speech system. The two systems were trained with the same manually labeled speech database.

This letter describes a two-band excitation model for HMM-based speech synthesis. The HMM-based speech synthesis system generates speech from the HMM training data of the spectral and excitation parameters. Synthesized speech has a typical quality of "vocoded sound" mostly because of the simple excitation model with the voiced/unvoiced selection. In this letter, two-band excitation based on the harmonic plus noise speech model is proposed for generating the mixed excitation source. With this model, we can generate the mixed excitation more accurately and reduce the memory for the trained excitation data as well.