# MLLP-VRAIN Spanish ASR Systems for the Albayzin-RTVE 2020 Speech-to-Text Challenge: Extension
## 5 Conclusions  
In this work, we have first described our four ASR systems that participated in the Albayzin-RTVE 2020 Speech-to-Text Challenge. The primary one, a streaming-enabled performance-focused hybrid ASR system (_p-streaming_1500 ms_ntl_) provided a good score of 16.0 WER points on the _test-2020_ set, and a remarkable 28% relative WER improvement over the 2018 winning ASR system on _test-2018_, with a theoretical latency of 1.5 s. Nearly the same performance was delivered by our first contrastive system (_c1-streaming_1500 ms_ntl_): 16.1 WER points on _test-2020_, at a significantly lower computational cost. In pursuit of low, state-of-the-art system latencies, our second contrastive system (_c2-streaming_600 ms_t_) provided a groundbreaking WER-latency balance, with a solid performance of 16.9 WER points on _test-2020_ at an empirical latency of 0.81 \(\pm\) 0.09 s (mean \(\pm\) stdev). Finally, our fourth ASR system was a contrastive off-line ASR system with VAD (_c3-offline_) providing the highest, yet still competitive, WER score of 17.1 points, attributable to an improvable VAD module and to the limitations of Full Sequence Normalization when dealing with local acoustic condition changes. Apart from the four ASR systems participating in the 2020 (open) challenge, two additional streaming systems have been described which, for better comparison with results from the 2018 challenge, were trained under the 2018 closed data conditions (about 5% and 2%, respectively, of the speech and text data we used in 2020). The first, latency-focused system (_closed-streaming_600 ms_nt2018_) achieves 23.5 WER points on test-2020, while the second, performance-focused system (_closed-streaming_1500 ms_nt2018_) reduces this figure to 23.1 WER points. Moreover, when using these systems with unconstrained language models (i.e., trained with all the text data used in 2020), these figures are further reduced to 20.4 (at 600 ms latency) and 19.9 (at 1500 ms latency). It is worth noting that these WER figures have been achieved under streaming conditions using only 205 h of RTVE2018 speech training data, thus not including any post-2018 RTVE speech data, and accounting for only 5% of the training data used in the 2020 open challenge. Nevertheless, these WER figures are not far behind those of the second-best 2020 open-condition system (19.3) and they are significantly ahead of those reported by other participants. Conceptualization, P.B.-A., J.J., A.G., J.A.S.-C., J.C., A.S. and A.J. ; methodology, P.B.-A., J.J., A.G., J.A.S.-C., A.S. and A.J. ; software, P.B.-A., J.J., A.G., J.I.-S., A.P. and J.A.S.-C.; validation, P.B.-A., J.J., A.G., J.A.S.-C., A.S. and A.J. ; formal analysis, P.B.-A., J.J., A.G., J.A.S.-C., A.S. and A.J. ; investigation, P.B.-A., J.J., A.G.; resources, A.P., J.A.S.-C.; data curation, J.A.S.-C.; writing--original draft preparation, [PERSON]-A., J.J., J.A.S.-C.; writing--review and editing, [PERSON]-A., J.J., A.G., J.I.-S., G.V.G.D.-M., J.A.S.-C., A.S. and A.J. ; visualization, P.B.-A., J.J., J.A.S.-C.; supervision, J.A.S.-[PERSON], [PERSON] and [PERSON]; provide acquisition, [PERSON]-A., [PERSON], [PERSON],[PERSON], [PERSON], [PERSON]-[PERSON], [PERSON], [PERSON] and A.J. All authors have read and agreed to the published version of the manuscript. The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements no. 761758 (X5 Gon) and 952215 (TAILOR), and Erasmus+ Education programme under grant agreement no. 20-226-093604-SCH (EXPExt); the Government of Spain's grant RTI2018-094879-B-I00 (Multisub) funded by MCIN/AEI/10.13039/501100011033 & \"ERDF A way of making Europe\", and FPU scholarships FPU14/03981 and FPU18/04135; the Generalitat Valenciana's research project Classroom Activity Recognition (ref. PROMETEO/2019/111), and predoctoral research scholarship ACIF/2017/055; and the Universitat Politecnica de Valencia's PAID-01-17 R&D support programme. Not applicable.