# Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database +
## 5 Results and Resources
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**AM Architecture** & **Model** & **Type** & **WER** \\ \hline DNN-HMM & Multistream CNN & novel & **17.60** \\  & CNN-TDNN-F & baseline & 19.27 \\ \hline Quartznet & Q15\(\times\)5 & novel & **22.95** \\  & Q5\(\times\)5 & baseline & 28.42 \\ \hline Wav2 vec2.0 & Wav2 Vec2-XLS-R-300M & novel & **20.68** \\  & (fine-tuned) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Total WER results per system on the whole Albayzin-RTVE 2020 testset. The results of the novel and evolved ASR engines are marked in bold. and conditions, as it was described in Section 4.1.3. In this case, it is worth mentioning how a fine-tuned Wav2 Vec2-XLS-R-300M model improved the performance of the Q15\(\times\)5 model, even though the former was fine-tuned for only 50 epochs and the Quartznet model was trained for a total of 300 epochs. This way, the Wav2 Vec2-XLS-R-300M model fine-tuned with the in-domain data reached a very promising 20.68 of WER. This result clearly demonstrates the power of the self-supervised models trained with huge amounts of unlabelled data, while maintaining similar inference latencies in comparison with the lighter Quartznet architectures (see Table 4). For the language model, the same 5-gram based external language model was employed. In Table 5, the total WER results obtained by the systems for each TV program in the RTVE2020 dataset are presented. As it can be observed in Table 5, the systems perform consistently along all the contents in the RTVE2020 dataset. The Multistream CNN based system obtained the best results for all the contents except for _Millenium_, for which the baseline CNN-TDNN-F system performed the best, and for _Impresciindibles_ where Wav2 vec2.0 performed slightly better. In contrast, the Quartznet Q5\(\times\)5 is the system that gives the worst performance in all the contents. The rest of the systems maintain consistency for most TV programs with respect to each particular total WER. The Wav2 vec2.0 based system outperforms the Quartznet Q15\(\times\)5 based ASR engine in all cases except one (_Millenium_), although the difference of 0.27 can be considered as negligible. In the rest of the contents, the differences between these two E2E systems are remarkable, although the Wav2 vec2.0 system was fine-tuned with six times fewer epochs than the Quartznet Q15\(\times\)5 based system. The greatest differences between both E2E systems are achieved in contents _Vaya crack_ and _Impresciindibles_, which were classified as two of the most complex programs given their spontaneous style and far-field low quality, respectively. In general, the behaviour of the systems regarding the content profiles is as expected. In those programs with cleaner speech, the WER decreases significantly compared to other programs which included adverse acoustic conditions, overlapping or spontaneous speech. More specifically, in TV shows such as _Aqui la Tierra_, _Los desayunos de RTVE_, _Millenium_ and _Version Espanola_, with controlled acoustic conditions (studio) and many segments with formal and well-structured speech, the error rates are below the 20% border and lower