The Hidden Markov Model Toolkit (HTK) is a portable toolkit for building and manipulating hidden Markov models. HTK is primarily used for speech recognition research although it has been used for numerous other applications including research into speech synthesis, character recognition and DNA sequencing. HTK is in use at hundreds of sites worldwide. HTK consists of a set of library modules and tools available in C source form. The tools provide sophisticated facilities for speech analysis, HMM training, testing and results analysis. The software supports HMMs using both continuous density mixture Gaussians and discrete distributions and can be used to build complex HMM systems. The HTK release contains extensive documentation and examples. HTK was originally developed at the Machine Intelligence Laboratory (formerly known as the Speech Vision and Robotics Group) of the Cambridge University Engineering Department (CUED) where it has been used to build CUED’s large vocabulary speech recognition systems (see CUED HTK LVR). In 1993 Entropic Research Laboratory Inc. acquired the rights to sell HTK and the development of HTK was fully transferred to Entropic in 1995 when the Entropic Cambridge Research Laboratory Ltd was established. HTK was sold by Entropic until 1999 when Microsoft bought Entropic. Microsoft has now licensed HTK back to CUED and is providing support so that CUED can redistribute HTK and provide development support via the HTK3 web site. See History of HTK for more details. While Microsoft retains the copyright to the original HTK code, everybody is encouraged to make changes to the source code and contribute them for inclusion in HTK3.

References in zbMATH (referenced in 15 articles )

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  1. Sano, Shotaro; Otsuka, Takuma; Okuno, Hiroshi G.: Solving Google’s continuous audio CAPTCHA with HMM-based automatic speech recognition (2013)
  2. Pittermann, Johannes; Pittermann, Angela; Minker, Wolfgang: Emotion recognition and adaptation in spoken dialogue systems (2010)
  3. Hyassat, Hussein; Abu Zitar, Raed: Arabic speech recognition using SPHINX engine (2008)
  4. Phua, Koksoon; Chen, Jianfeng; Dat, Tran Huy; Shue, Louis: Heart sound as a biometric (2008)
  5. Gales, Mark; Young, Steve: The application of hidden Markov models in speech recognition (2007)
  6. Sparacino, Flavia: Natural interaction in intelligent spaces: Designing for architecture and entertainment (2007)
  7. Tan, Alan W.C.; Rao, M.V.C.; Sagar, B.S.Daya: A signal subspace approach for speech modelling and classification (2007)
  8. Xu, Haitian; Tan, Zheng-Hua; Dalsgaard, Paul; Mattethat, Ralf; Lindberg, Børge: A configurable distributed speech recognition system (2007)
  9. Günter, Simon; Bunke, Horst: HMM-based handwritten word recognition: on the optimization of the number of states, training iterations and Gaussian components (2004)
  10. Bailly, G.: Close shadowing natural versus synthetic speech (2003)
  11. Mouria-beji, Fériel: A hierarchical Bayesian model for continuous speech recognition (2002)
  12. Amengual, Juan Carlos; Castaño, Asunción; Castellanos, Antonio; Jiménez, Victor M.; Llorens, David; Marzal, Andrés; Prat, Federico; Vilar, Juan Miguel; Benedí, José Miguel; Casacuberta, Francisco; Pastor, Moisés; Vidal, Enrique: The EuTrans-I spoken language translation system (2000)
  13. Young, Steve: Acoustic modelling for large vocabulary continuous speech recognition (1999)
  14. Knill, K.; Young, S.: Hidden Markov models in speech and language processing (1997)
  15. Starner, Thad; Weaver, Joshua; Pentland, Alex: A wearable computer-based American sign language recogniser. (1997)