Linear dynamical models in speech synthesisLinear dynamical models in speech synthesis Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2015-11-082014enHidden Markov models (HMMs) are becoming the dominant approach for text-to-speech synthesis (TTS). HMMs provide an attractive acoustic modeling scheme which has been exhaustively investigated and developed for many years. Modern HMM-based speech synthesizers have approached the quality of the best state-of-the-art unit selection systems. However, we believe that statistical parametric speech synthesis has not reached its potential, since HMMs are limited by several assumptions which do not apply to the properties of speech. We, therefore, propose in this paper to use Lin-ear Dynamical Models (LDMs) instead of HMMs. LDMs can better model the dynamics of speech and can produce a naturally smoother trajectory of the synthesized speech. We perform a series of experiments using different system configurations to check on the performance of LDMs for speech synthesis. We show that LDM-based synthesizers can outperform HMM-based ones in terms of cepstral distance and are a very promising acoustic modeling alternative for statistical parametric TTS. http://creativecommons.org/licenses/by/4.0/IEEE International Conference on Acoustic, Speech and Signal Processing Tsiaras Vasileios Τσιαρας Βασιλειος Ranniery Maia Diakoloukas Vasilis Διακολουκας Βασιλeioς Stylianou, Yannis Digalakis Vasilis Διγαλακης Βασιλης Institute of Electrical and Electronics Engineers HMMs (Hidden Markov models) hidden markov models hmms hidden markov models