Enhancement of spectral clarity for HMM-based text-to-speech systems

This paper proposes a method to enhance the spectral clarity of hidden Markov model (HMM)-based text-to-speech (TTS) systems. A simple way of enhancing spectral clarity is increasing the order of spectral parameters in the speech analysis/synthesis stage, but the method has an inherent statistical modeling problem. The proposed algorithm takes a low-to-high-order spectral parameter mapping approach that adopts low-order parameters for HMM training but does high-order parameters for the actual synthesis step. Various ways of mapping criterion to find appropriate high-order parameters are investigated to further enhance the quality of synthesized speech. Performance evaluation results verify the superiority of the proposed method compared to the conventional one.