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Global Variance in Speech Synthesis With Linear Dynamical Models.

, , , , and . IEEE Signal Process. Lett., 23 (8): 1057-1061 (2016)

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Maximum-likelihood stochastic transformations for continuous speech recognition. Technical University of Crete, Greece, (2000)National Archive of PhD Theses: oai:10442/32036.A Case Study on the Importance of Belief State Representation for Dialogue Policy Management., , , , and . INTERSPEECH, page 986-990. ISCA, (2018)Maximum likelihood stochastic transformation adaptation for medium and small data sets., , and . Comput. Speech Lang., 15 (3): 257-285 (2001)A sub-optimal viterbi-like search for linear dynamic models classification., , and . INTERSPEECH, page 1717-1720. ISCA, (2007)Linear dynamical models in speech synthesis., , , , and . ICASSP, page 300-304. IEEE, (2014)Towards a linear dynamical model based speech synthesizer., , , , and . INTERSPEECH, page 1221-1225. ISCA, (2015)Gaussian Mixture Clustering and Language Adaptation for the Development of a New Language Speech Recognition System., , , and . IEEE Trans. Speech Audio Process., 15 (3): 928-938 (2007)Maximum-likelihood stochastic-transformation adaptation of hidden Markov models., and . IEEE Trans. Speech Audio Process., 7 (2): 177-187 (1999)Variational Denoising Autoencoders and Least-Squares Policy Iteration for Statistical Dialogue Managers., , , and . IEEE Signal Process. Lett., (2020)Rapid acoustic model development using Gaussian mixture clustering and language adaptation., , , and . INTERSPEECH, page 17-20. ISCA, (2004)