## 4 Submitted Systems
### Speech-to-Text Challenge
#### 4.1.1 Open-Set Condition Systems
The second contrastive system was composed of a bidirectional LSTM-HMM (long short term memory-hidden Markov model) acoustic model combined with a three-gram language model for decoding and a nine-gram language for re-scoring lattices. The acoustic model was trained with the same data as the primary system of the open-set condition. The language models were estimated with the RTVE2018 database text files and general news data from newspapers. * G7-MLLP-RWTH [21]. MLLP, Machine Learning and Language Processing, Universidad Politecnica de Valencia, Spain and Human Language Technology and Pattern Recognition, RWTH Aachen University, Germany. G7-MLLP-RWTH submitted only a primary system. The recognition engine was an evolution of RETURNN [[https://github.com/rwth-i6/returnn](https://github.com/rwth-i6/returnn)]([https://github.com/rwth-i6/returnn](https://github.com/rwth-i6/returnn)) and RASR [[https://www-i6.informatik.rwth-aachen.de/rwth-asr/](https://www-i6.informatik.rwth-aachen.de/rwth-asr/)]([https://www-i6.informatik.rwth-aachen.de/rwth-asr/](https://www-i6.informatik.rwth-aachen.de/rwth-asr/)), and it was based on a hybrid LSTM-HMM acoustic model. Acoustic modeling was done using a bidirectional LSTM network with four layers and 512 LSTM units in each layer. Three-thousand eight-hundred hours of speech transcribed from various sources (subtitled videos of Spanish and Latin American websites) were used for training the acoustic models. The language model for the single-pass HMM decoding was a five-gram count model trained with Kneser-Ney smoothing on a large body of text data collected from multiple publicly available sources. A lexicon of 325K words with one or more variants of pronunciation was used. Neither speaker, nor domain adaptation, nor model tuning were used. * G14-SIGMA [22]. Sigma AI, Madrid, Spain. G14-SIGMA submitted only a primary system. The ASR system was based on the open-source Kaldi Toolkit. The ASR architecture consisted of the classical sequence of three main modules: an acoustic model, a dictionary or pronunciation lexicon, and an N-gram language model. These modules were combined for training and decoding using weighted finite state transducers (WFST). The acoustic modeling was based on deep neural networks and hidden Markov models (DNNHMM). To improve robustness mainly on speaker variability, speaker adaptive training (SAT) based on i-vectors was also implemented. Acoustic models were trained using 600 h from RTVE2018 (350 h of manual transcription), VESLIM [[https://ieeexplore.ieee.org/document/1255449/](https://ieeexplore.ieee.org/document/1255449/)]([https://ieeexplore.ieee.org/document/1255449/](https://ieeexplore.ieee.org/document/1255449/)) (103 h), and OWNMEDIA (162 h of television programs). RTVE2018 database texts, news between 2015 and 2018, interviews, and subtitles were used to train the language model. * G21-EMPHATIC [23]. SPIN-Speech Interactive Research Group, Universidad del Pais Vasco, Spain and Intelligent Voice, U.K. The G21-EMPHATIC ASR system was based on the open-source Kaldi Toolkit. The Kaldi Aspire recipe [[https://github.com/kaldi-asr/kaldi/tree/master/egs/aspire](https://github.com/kaldi-asr/kaldi/tree/master/egs/aspire)]([https://github.com/kaldi-asr/kaldi/tree/master/egs/aspire](https://github.com/kaldi-asr/kaldi/tree/master/egs/aspire)) was used for building the DNNHMM acoustic model. [PERSON], [PERSON] [[http://universal.elra.info/product_info.php?](http://universal.elra.info/product_info.php?)]([http://universal.elra.info/product_info.php?](http://universal.elra.info/product_info.php? ))products_id=1421, CORLEC-EHU [[http://gfts.ehu.es/gfts/NT/fulltext/RodriguezEtal03a.pdf](http://gfts.ehu.es/gfts/NT/fulltext/RodriguezEtal03a.pdf)]([http://gfts.ehu.es/gfts/NT/fulltext/RodriguezEtal03a.pdf](http://gfts.ehu.es/gfts/NT/fulltext/RodriguezEtal03a.pdf)), and TC-START databases with a total of 352 h were used to train the acoustic models. The provided training and development audio files were subsampled to 8 kHz before being used in the training and testing processes.