Search Results for "hmi linux"
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Showing 8 open source projects for "hmi linux"
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Source code from the Research Institute for Signals, Systems and Computational Intelligence http://fich.unl.edu.ar/sinc
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AhoTTS - TTS for Basque and Spanish
Text-to-Speech for Basque and Spanish
Text-to-Speech conversor for Basque and Spanish. It includes linguistic processing and built voices for the languages aforementioned. Its acoustic engine is based on hts_engine and it uses a high quality vocoder called AhoCoder. Developed by Aholab Signal Processing Laboratory: https://aholab.ehu.es/aholab/ http://aholab.ehu.es/ahocoder/ -
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AhoTTS Multilingual, a Multilingual TTS
Text-to-Speech TTS for Basque, Spanish, Catalan, Galician and English
Text-to-Speech conversor for Basque, Spanish, Catalan, Galician and English. It includes linguistic processing and built voices for all the languages aforementioned. Its acoustic engine is based on hts_engine and it uses a high quality vocoder called AhoCoder. Developed by Aholab Signal Processing Laboratory: https://aholab.ehu.es/aholab/ http://aholab.ehu.es/ahocoder/ -
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AhoTTS Iparrahotsa
TTS for Basque Lapurdian dialect
AhoTTS Iparrahotsa is the TTS developed at the Aholab Signal Processing Laboratory of the University of the Basque Country (UPV/EHU) for the Lapurdian dialect of Basque. This dialect is spoken at the Northern area of the Basque speaking area (French region). This project was funded by the Euroregion Aquitaine-Euskadi under grant EUSKADI-2012-004. -
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Dhvani is Text-to-Speech System for Indic Languages. Current C- GNU/Linux implementation supports Hindi, Kannada, Marathi, Malayalam, Gujarati, Bengali, Telugu, Panjabi, Tamil and Oriya.
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Scalable Language API (SLAPI) The most comprehensive architecture for conversational natural-language applications including speech recognition/synthesis, semantics, & machine translation. Used on Android & other mobile app platforms.
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VoxForge collects user-submitted speech audio files for the creation of Acoustic Models for Free and Open Source Speech Recognition Engines such as HTK, Julius, ISIP and Sphinx.
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This project will show how to implement the Hidden Markov Model approximations of Voice Recognition into embedded and low power systems.
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