* History [#af7558b8]
 #contents
 ** 2006 [#z5d7dda6]
 >''December 29:''
 >> HTS version 2.0 was '''finally''' released :-)~
 The new features are
 - Based on [[HTK-3.4>http://htk.eng.cam.ac.uk/download.shtml]].
 - Compilation without [[SPTK>http://kt-lab.ics.nitech.ac.jp/~tokuda/SPTK/index.html]].
 - Compilation without [[SPTK>http://www.sp.nitech.ac.jp/~tokuda/SPTK/index.html]].
 - The [[license of HTS>License]] was slightly modified.
 - Support gcc4.
 - Thousands of fixed bugs.
 - HRest can generate state duration densities (-g option).
 - Model boundaries can be given to HERest (-e option).~
 We may specify a part of model boundaries (e.g, pause positions).
 - Reduced-memory implementation of context clustering in HHEd (-r option).
 - Each decision tree can have a name with regular expression (-p option).~
  TB 000 {(*-a+*, *-i+*, *-u+*).state[2]}
  TB 000 {(*-sil+*, *-pau+*).state[3]}
 As a result, different two trees can be constructed for consonants and vowels, respectively.
 - The interface of HMGenS has been switched from ''HHEd-style'' to ''HERest-style''.
 - Flexible model structures in HMGenS (in the previous version, the first stream is assumed as mcep, and the others are assumed as log F0).  Non-left-to-right models and full covariance matrices for state output pdfs can also be used.
 - EM-based parameter generation algorithm (-c option), i.e, mixture of Gaussians can be used. 
 -- -c 0: Cholesky decomposition
 -- -c 1: EM (with fixed state sequence)
 -- -c 2: EM (phone boundaries can be given with -e option)
 - Random generation algorithm is also supported (set config. variable RNDPG = TRUE).
 - Speaker adaptation, adaptive training, and semi-tied covariance transforms are supported for multi-stream HMMs/MSD-HMMs. 
 -- MLLRMEAN, MLLRCOV, and CMLLR-based adaptation.
 -- CMLLR-based adaptive training.
 -- Decision trees for context clustering can be used to define regression classes for adaptation.
 -- HMGenS can read MLLRMEAN, MLLRCOV, CMLLR, and SEMIT transforms for adaptation.
 - MAP adaptation is also supported.
 - Performance improvements in hts_engine.
 - Miscellaneous changes.
 
 >''December 4:''
 >> HTS version 2.0RC3 was released to members of Mailing List.
 >''July 1:''
 >> HTS version 2.0RC2 was released to members of Mailing List.
 >''March 3:''
 >> HTS version 2.0RC1 was released to members of Mailing List.
 >''February 15:''
 >> HTS version 2.0RC0 was released to the internal working group. 
 
 ** 2003 [#ld26312a]
 >''December 26:''
 >> HTS version 1.1.1 was released. The new features were
 - Based on HTK-3.2.1
 - Demo script for ARCTIC database
 - Demo script for an original database (Japanese)
 - Variance flooring in demo script
 - Postfiltering in hts-engine
 - Many fixed bugs
 
 >''Oct. 14:''
 >> New HTS voices trained by ARCTIC databases were released. 
 >''June 11:''
 >>  HTS version 1.1b was released.
 >''May 9:''
 >> HTS version 1.1 was released. The new features were
 - A small synthesis engine (to be called from Festival).
 - HMM file format converter for the engine.
 - Many fixed bugs (Thanks for reporting them).
 - Accompanied by HTS voices for Festival.
 
 >''January 21:''
 >> Minor revision was made to HTS version 1.0. 
 
 ** 2002 [#s0e2a8a8]
 >''December 25:''
 >> HTS version 1.0 was released.
 //>''September:''
 //>> The first paper about eigenvoice for HMM-based speech synthesis was //appeared in ICSLP'02.
 //
 //** 1999 [#v67c04e5]
 //> ''September:''
 //>> The first paper about the current HTS framework was appeared in //Eurospeech'99. 
 //> ''March:''
 //>> The first paper about MSD-HMM was appeared in ICASSP'99.
 //
 //** 1998 [#v760e959]
 //> ''November:''
 //>> The first paper about speaker adaptation for the HMM-based speech synthesis //using MLLR was appeared in ESCA/COCOSDA workshop on speech synthesis.
 //
 //** 1997 [#v95967aa]
 //> ''September:''
 //>> The first paper about speaker interpolation for the HMM-based speech //synthesis was appeared in Eurospeech'97.
 //
 ** 1995 [#v5285cba]
 > ''May:''
 >> The first paper about the speech parameter generation algorithm was appeared  in ICASSP'95.

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