# Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database +
## 5 Results and Resources  
In the following Table 4, the total WER values are presented for each system over all the TV programs in the RTVE2020 database. The first objective of this work was focused on improving our initially best CNN-TDNN-F based ASR engine, presented as the primary system to the Albayzin-RTVE 2020 S2T Transcription Challenge and which scored the second position among all the systems presented to competition. Although these improvements could be focused on improving both acoustic and language models, we decided to put our efforts into enhancing the acoustic model, which probably poses the most challenging task, while maintaining the same Kaldi based ASR architecture. Moreover, one of the main conclusions of the previous study of the authors [7] was that improving acoustic models helped our ASR engines more than language models, since most of the evaluation contents included spontaneous speech and our text corpus was mainly integrated by text contents with formal language. Given the difficulty of the RTVE2020 evaluation dataset at the acoustic and phonemic level, we decided to evaluate a more complex CNN and TDNN-F based neural network. This way, the Multistream CNN architecture clearly improved the performance of the CNN-TDNN-F acoustic model by tripling the stack of TDNN-F layers with diverse temporal resolutions. Therefore, replacing the CNN-TDNN-F acoustic model by the novel Multistream CNN one and maintaining the same lexicon and language models for decoding (3-gram model) and rescoring (4-gram based RNNLM model) the initial lattices, the results improved from 19.27 to a very competitive 17.60 of WER. The second objective of this work was to see how we could improve the performance of the Nuance's Quartznet Q5\(\times\)5 based system. However, analysing the state of the art, currently these E2E systems do not perform as well as the Kaldi based engines, the Quartznet architectures present an interesting proposal to integrate ASR functionalities in embedded systems considering the scarce HW resources and the low inference times required. With the aim of improving the results obtained by the Q5\(\times\)5 based system, we included three principal improvements. First, we extended the architecture by adding two more groups of blocks and thus building a bigger Q15\(\times\)5 architecture based ASR system. The second improvement corresponded to the inclusion of a triangular cyclical learning rate policy. This method [32] lets the learning rate vary cyclically between reasonable boundary values instead of monotonically decreasing it during training, thus improving classification accuracy. Finally, we included more training epochs by extending the 100 epochs employed for the Q5\(\times\)5 network to the 300 training epochs used for the new Q15\(\times\)5 acoustic model. Applying these evolutions, we managed to improve the error rate in 5.47 points, considering the 28.42 of WER achieved by the Q5\(\times\)5 baseline and the new 22.95 of WER obtained by the Q15\(\times\)5 based novel architecture. The 5-gram based external language model remained the same for both systems. The last objective of this study was to evaluate the performance of the self-supervised Wav2 vec2.0 model in these challenging scenarios. Although these types of models seem to be more focused on being applied in situations in which there is not enough annotated in-domain data to train acoustic models from scratch, its advantages such as (i) the clarity of the architecture, (ii) taking advantage of the extensive acoustic knowledge obtained by the pre-trained model, and (iii) its low latency in inference, make it a very interesting ASR system to explore. To this end, we selected the Wav2 Vec2-XLS-R-300M pre-trained model, which was pre-trained with 436k hours of unlabelled speech data from diverse corpora