## 4 Results
### End-to-End LF-MMI ASR  
Our PyChain-based baseline had a good performance in relation to the number of parameters and the easier training process compared to other end-to-end frameworks. The WER achieved for standard TV news (e.g., 20H, LN24H) was between 23% and 26%, as Table 2 shows. These results are within the expected range where commercial ASR systems operate. To compare both hybrid and end-to-end systems, we also trained a hybrid model by using only the RTVE_train100 partition. In this case, multi-condition data augmentation was not applied. The PyChain-based system was still far from the Kaldi-based hybrid system, with a WER increase of 17% in the worst-case scenario. This gap could be reduced with the application of some data augmentation techniques (e.g., speed or volume pertur \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **20H\_dev1** & **AP\_dev1** & **CA\_dev1** & **LM\_dev1** & **Mill\_dev1** & **LN24H\_dev1** \\ \hline
**Hybrid ASR** & & & & & & \\ Kaldi-based baseline [32] & 14.88 & 20.94 & 49.55 & 21.44 & 17.01 & 24.13 \\ Reverb. data augmentation & **14.76** & 21.00 & **44.69** & **21.03** & **16.42** & **23.62** \\ Kaldi-based baseline (RTVE\_train100) & 16.09 & 22.32 & 51.23 & 23.02 & 17.70 & 25.53 \\
**End-to-end LF-MMI ASR** & & & & & & \\ PyChain-based baseline & 23.66 & 33.31 & 59.34 & 29.95 & 35.09 & 25.08 \\ Domain adversarial training & **23.53** & **32.99** & **59.25** & **29.91** & **34.67** & 25.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: WER (%) on the different datasets for hybrid and end-to-end ASR systems. Each one of the evaluation datasets contains one hour of speech. In bold, the improvements of the Kaldi-based system after applying reverberation, and the improvements related to the Pychain-based system after applying DAT. \begin{table}
\begin{tabular}{c c} \hline \hline  & **RTVE2020\_test** \\ \hline
**Hybrid ASR** & & & \\ Kaldi-based baseline [32] & 31.01 \\ Reverb. data augmentation & & & 27.68 \\
**End-to-end LF-MMI ASR** & & & & \\ PyChain-based baseline & 40.90 & Domain adversarial training & 42.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: WER (%) on the RTVE2020 test partition for all the systems. Results were obtained after the submission. bation). Despite the fact that augmented data caused a slight improvement of the WER for the PyChain system [18], TV shows can contain some acoustic characteristics that could be better modeled by some audio perturbations. On the other hand, we evaluated the effect of learning acoustic representations invariant to the TV show domain. After applying DAT, the results in Table 2 showed improvements, in terms of the WER, up to 2.87% as compared to the PyChain-based baseline. We are aware that those results are still far from the Kaldi-based hybrid ASR system based on the DNN-HMM. Nevertheless, the results in Table 2 gave us an insight into how the use of DAT in the end-to-end LF-MMI model can improve its performance in most of the scenarios. Furthermore, it seemed that DAT was able to generate deep acoustic features invariant to different TV shows with different acoustic conditions without the need for data augmentation techniques. In addition, Table 3 shows that applying DAT did not reduce the WER on the RTVE2020 test partition. The main reason was DAT alleviated the labeled domain conditions in the training dataset. Thus, invariant features were trained without regarding these unseen external factors. Finally, regarding computational requirements for speech transcription, we considered that PyChain carries out two main stages: a first decoding stage and a second four-gram rescoring stage. Real-Time factors (RT) for different test partitions are presented in Table 4. In all cases, the two GPU resources described in Section 3.4 were used. The results in Table 4 show that time requirements depend on the characteristics of the test partitions. Less time is required to transcribe the best acoustic conditions and less challenging scenarios.