This paper describes the Speech-to-Text system developed by the Multimedia Technologies Group (GTM) of the atlanTTic research center at the University of Vigo, for the Albayzin Speech-to-Text Challenge (S2T) organized in the Iberspeech 2018 conference. The large vocabulary automatic speech recognition system is built using the Kaldi toolkit. It uses an hybrid Deep Neural Network - Hidden Markov Model (DNN-HMM) for acoustic modeling, and a rescoring of a trigram based word-lattices, obtained in a first decoding stage, with a fourgram language model or a language model based on a recurrent neural network. The system was evaluated only on the open set training condition.
Cite as: Docío-Fernández, L., García-Mateo, C. (2018) The GTM-UVIGO System for Albayzin 2018 Speech-to-Text Evaluation. Proc. IberSPEECH 2018, 277-280, doi: 10.21437/IberSPEECH.2018-58
@inproceedings{dociofernandez18_iberspeech,
author={Laura Docío-Fernández and Carmen García-Mateo},
title={{The GTM-UVIGO System for Albayzin 2018 Speech-to-Text Evaluation}},
year=2018,
booktitle={Proc. IberSPEECH 2018},
pages={277--280},
doi={10.21437/IberSPEECH.2018-58}
}