## 4 Submitted Systems
### Speech-to-Text Challenge
#### 4.1.1 Open-Set Condition Systems  
* G1-GTM-UVIGO [19]. Multimedia Technologies Group, Universidad de Vigo, Spain. G1-GTM-UVIGO submitted two systems using as the recognition engine the Kaldi toolkit [[http://kaldi-asr.org/](http://kaldi-asr.org/)]([http://kaldi-asr.org/](http://kaldi-asr.org/)). Primary and contrastive systems differed in the language model (LM) used in the rescoring stage. The primary system used the four-gram LM, and the contrastive system used the recurrent neural network language modeling toolkit (RNNLM) provided in the Kaldi distribution. The acoustic models were trained using 109 h of speech: 79 h in Spanish (2006 TC-START [[http://tcstar.org/](http://tcstar.org/)]([http://tcstar.org/](http://tcstar.org/))) and 30 in Galician (news database of Galicia, Transcrigal [[http://metashare.elda.org/repository/browse/transcrigal-db/72](http://metashare.elda.org/repository/browse/transcrigal-db/72) ee3974 cbec11e181b50030482 ab95203851f1f95e64c00b842977a318 ef641/]([http://metashare.elda.org/repository/browse/transcrigal-db/72](http://metashare.elda.org/repository/browse/transcrigal-db/72) ee3974 cbec11e181b50030482 ab95203851f1f95e64c00b842977a318 ef641/)). The RTVE2018 database text files and text corpus of 90M words from several sources were used for language model training. * G3-GTTS-EHU. Working group on software technologies, Universidad del Pais Vasco, Spain. This team participated with a commercial speech-to-text conversion system, with general purpose acoustic and language models. Only a primary system was submitted. * G5-LIMECRAFT. Visiona Ingenieria de Proyectos, Madrid, Spain. This team participated with a commercial speech-to-text conversion system, with general purpose acoustic and language models. Only a primary system was submitted. * G6-VICOMTECH-PRHLT [20]. VICOMTECH, Visual Interacion & Communication Technologies, Donostia, Spain and Pattern Recognition and PRHLT, Human Language Technologies Research Center, Universidad Politecnica de Valencia, Spain. G6-VICOMTECH-PRHLT submitted three systems. The primary system was an evolution of an already existing E2E (end-to-end) model based on DeepSpeech2 [[https://arxiv.org/abs/1512.02595](https://arxiv.org/abs/1512.02595)]([https://arxiv.org/abs/1512.02595](https://arxiv.org/abs/1512.02595)), which was built using the three-fold augmented SAVAS [[https://cordis.europa.eu/project/rcn/103572/factsheet/en](https://cordis.europa.eu/project/rcn/103572/factsheet/en)]([https://cordis.europa.eu/project/rcn/103572/factsheet/en](https://cordis.europa.eu/project/rcn/103572/factsheet/en)), Albayzin [[http://catalogue.elra.info/en-us/repository/browse/ELRA-S0089/](http://catalogue.elra.info/en-us/repository/browse/ELRA-S0089/)]([http://catalogue.elra.info/en-us/repository/browse/ELRA-S0089/](http://catalogue.elra.info/en-us/repository/browse/ELRA-S0089/)), and Multext [[http://catalogue.elra.info/en-us/repository/browse/ELRA-S0060/](http://catalogue.elra.info/en-us/repository/browse/ELRA-S0060/)]([http://catalogue.elra.info/en-us/repository/browse/ELRA-S0060/](http://catalogue.elra.info/en-us/repository/browse/ELRA-S0060/)) corpora for 28 epochs. For this challenge, it evolved with two new epochs using the same corpora in addition to the three-fold augmented nearly-perfectly aligned corpus obtained from the RTVE2018 dataset. A total of 897 h was used for training. The language model was a five-gram, trained with the text data from the open-set dataset. The first contrastive system was also an evolution of the already existing E2E model, but in this case, it evolved with one epoch using the three-fold augmented corpora used in the primary system and a new YouTube RTVE corpus. The duration of the total amount of training audio was 1488 h. The language model was a five-gram trained with the text data from the open-set dataset.