HMM-based Speech Synthesis Adapted to Listeners’ & Talkers’ conditions Dr Junichi Yamagishi The Centre for Speech Technology Research University of Edinburgh www.cstr.ed.ac.uk

Source URL: listening-talker.org

Prediction of Nuclear Localization Signals by HMM Keun-Joon Park Minoru Kanehisa

Source URL: www.jsbi.org

Deep neural network context embeddings for model selection in rich-context HMM synthesis Thomas Merritt1 , Junichi Yamagishi1,2 , Zhizheng Wu1 , Oliver Watts1 , Simon King1 1 The Centre for Speech Technology Research, U

Source URL: www.cstr.ed.ac.uk

• Computational linguistics
• Markov models
• Automatic identification and data capture
• Computer accessibility
• Speech recognition
• Speech synthesis
• Hidden Markov model
• KullbackLeibler divergence

Protein Sequence-Structure Alignment Using 3D-HMM Masashi Fujita

Source URL: www.jsbi.org

• Bioinformatics
• Sequence alignment
• Protein structure prediction
• Homology modeling
• BLAST
• Sequence analysis
• Structural alignment
• Hidden Markov model
• HH-suite
• Multiple sequence alignment

ATTRIBUTING MODELLING ERRORS IN HMM SYNTHESIS BY STEPPING GRADUALLY FROM NATURAL TO MODELLED SPEECH Thomas Merritt1 , Javier Latorre2 , Simon King1 1 2

Source URL: www.cstr.ed.ac.uk

• Computational linguistics
• Markov models
• Probability distributions
• Digital signal processing
• Standard deviation
• Hidden Markov model
• Speech synthesis
• Window function
• Variance
• Ping
• Normal distribution

Detection of Apoptotic Domains Against KEGG Database by the HMM Search Masahiro Hattori Minoru Kanehisa

Source URL: www.jsbi.org

GMM AND HMM TRAINING BY AGGREGATED EM ALGORITHM WITH INCREASED ENSEMBLE SIZES FOR ROBUST PARAMETER ESTIMATION Takahiro Shinozaki∗ , Tatsuya Kawahara Academic Center for Computing and Media Studies, Kyoto University, Ky

Source URL: www.ar.media.kyoto-u.ac.jp

6. AT WORK (Inside a convenience store. Tanaka and Ou are working.) (The break room.) Tanaka: Ou-san, Ou-san. Ou: Hmm?

Source URL: nihongo.hum.tmu.ac.jp

INTERSPEECHGaussian Mixture Optimization for HMM based on Efficient Cross-validation Takahiro Shinozaki∗ , Tatsuya Kawahara Academic Center for Computing and Media Studies, Kyoto University, Kyoto, Japan ∗

Source URL: www.ar.media.kyoto-u.ac.jp

2006 Paper 9 Question 13 Bioinformatics (a) Hidden Markov models (HMM) are widely used in Bioinformatics. (i ) In a HMM when would you use the Baum–Welch algorithm, and when the Viterbi algorithm, and why? Give biolog

Source URL: www.cl.cam.ac.uk