# Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database +
## 6 Conclusions and Future Work  
In this work, three novel ASR architectures have been presented and evaluated on the RTVE2020 database. These systems correspond to an evolution of two similar ASR engines, previously constructed and evaluated on the same database and which were selected as the baseline systems. The RTVE2020 database is the result of an interesting and necessary initiative to collect real broadcast Spanish speech data with the aim of building competitive ASR engines in this language. In conjunction to the RTVE2018 database [40], both constitute the larger, most complete and challenging speech corpus available for the community in the Spanish language. Over this interesting and challenging dataset, we explored different alternatives to outperform the initial DNN-HMM and Quartznet Q5\(\times\)5 based systems submitted to the Albayzin SZT Transcription Challenge 2020. In total, we presented three novel ASR engines, which clearly improved the performances of these baseline systems. The first system, based \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ASR System** & \begin{tabular}{c} **RAM** \\ **(GB)** \\ \end{tabular} & \
\begin{tabular}{c} **CPU** \\ **Cores** \\ \end{tabular} &
\begin{tabular}{c} **GPU** \\ **(GB)** \\ \end{tabular} & **Time** & **RTF** \\ \hline Multistream CNN & 6.7 & 1 & 12 & 4.9 h & 0.09 \\ \hline CNN-TDNN-F & 6.7 & 1 & 12 & 4.9 h & 0.09 \\ \hline Quartznet Q5\(\times\)5 & 6 & 1 & 12 & 8 h & 0.14 \\ \hline Quartznet Q5\(\times\)5 & 6 & 1 & 12 & 7.5 h & 0.13 \\ \hline Wav2 vec2.0 & 30 & 40 & 18 & 8 h & 0.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Processing time and computational resources needed by each submitted system in terms of RAM memory, CPU cores, GPU memory, decoding time and Real-Time Factor (RTF). on the novel Multistream CNN acoustic model, reached the best results for almost all the contents, while the Quartznet Q15\(\times\)5 outperformed the Q5\(\times\)5 model at almost six points of WER, by extending the size of the model, including more training epochs and applying a triangular cyclical method for the optimal learning rate calculation. In addition, we also evaluated the performance of the Wav2 vec2.0 self-supervised model, which achieved better results than the Quartznet based systems applying a fine-tuning process of only 50 epochs over the pre-trained model. In summary, this work constitutes an interesting and complete benchmark of several architectures in order to select the optimal ASR engine depending on the required quality, the available HW resources and the latency expected. As future work, once the acoustic models have been considerably enhanced, we will focus on improving the language models, by incorporating new rescoring processes based on Transformers or Self attentive simple Recurrent Unit architectures. It should also be interesting to explore strategies to reduce the size of the Multistream CNN acoustic model, by reducing the amount of TDNN-F layers and/or the internal cell dimensions without loss of quality. These techniques would probably reduce the size of the model and would allow for increasing the inference times. Moreover, bigger Wav2 vec2.0 pre-trained models will also be explored, which contained one and two billion parameters, with longer fine-tuning processes in order to allow the model to better adapt to the application domains. Finally, new self-supervised models like HuBERT [3] and SUPERB [41] will be also studied over the same dataset. Conceptualisation, A.A.; methodology, A.A.; software, H.A. and I.G.T. ; validation, A.A., H.A. and I.G.T. ; formal analysis, A.A. and I.G.T. ; investigation, A.A., H.A. and I.G.T. ; resources, A.A., H.A. and I.G.T. ; data curation, H.A. ; writing--original draft preparation, A.A.; writing--review and editing, A.A., H.A., I.G.T., A.G.-D.; visualisation, A.A.; supervision, A.A.; project administration, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript. The authors would like to thank [PERSON] for preparing the figures.