Silén, H., Helander, E., Nurminen, J., Koppinen, K., and Gabbouj, M.
Interspeech 2010, Makuhari, Japan
This page contains English samples of HMM-based unit selection using HMM models in cost computation and:
For both HMM-based unit selection and HMM-based synthesis, following parameterization was used:
The HMM-training is done using the HMM-based speech synthesis system HTS  (version 2.1) and an English speech database CMU ARCTIC (speaker slt) available in . Here, postfiltering was used instead of global variance.
 Kawahara, H., Masuda-Katsuse, I., and de Cheveigné, A., Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency-based F0 extraction: Possible role of a repetitive structure in sounds, Speech Communication 27, 1999, pp. 187-207.
 Fukada, T., Tokuda, K., Kobayashi, T., and Imai, S., An adaptive algorithm for mel-cepstral analysis of speech, in ICASSP, 1992, pp. 137-140.
 Yoshimura, T., Tokuda, K., Masuko, T., Kobayashi, T., and Kitamura, T., Mixed excitation for HMM-based speech synthesis, In EUROSPEECH, 2001, pp. 2263-2266.
 Zen, H., Nose, T., Yamagishi, J., Sako, S., Masuko, T., Black, A., and Tokuda, K., The HMM-based Speech Synthesis System (HTS) Version 2.0, in ISCA SSW6, 2006, pp.294-299.
 CMU_ARCTIC speech synthesis databases, available in http://festvox.org/cmu_arctic/.
 Toda, T. and Tokuda, K., Speech parameter generation algorithm considering global variance for HMM-based speech synthesis, in Interspeech, 2005, pp. 2801-2804.