# Accented Speech Recognition Based on End-to-End Domain Adversarial Training of Neural Networks
## 4 Experiments and Results
### Experimental Results
We investigated the results given in [6], in which the accent embedding technique, known as the most representative approach for accented speech recognition, was adopted; the end-to-end method was applied for training acoustic models; and the Common Voice corpus was used for evaluation. We selected the Indian accent for our performance comparison as it was the only target accent for which performance was reported in both studies. In the conventional study, the WER of the Indian accent was 52%, which outperformed our approach (63.56%). However, a direct comparison may not be meaningful, as the two studies conducted experiments on different experimental setups in terms of the training dataset and hyperparameters. For this reason, we concentrated on relative improvements in the baseline system. The baseline performance of the conventional study was 55.2%, providing a relative improvement of 6.15%. On the other hand, our approach achieved a relative improvement of 14.35% compared with the baseline. These results show that our proposed approach improved the baseline system more efficiently with less computational cost compared with the conventional study. The next experiment was conducted to investigate the effect of the amount of target domain data on performance improvement. The performance was observed when larger amounts of target domain data were applied to the DANN model. The model was called DANN-inc and it was compared with the DANN model (DANN) shown in Table 5. The Common Voice corpus provides different amounts of speech files according to accents. For a fair evaluation, we balanced the amount of data for different accent types when constructing the DANN-inc model. As a result, the amount of data for each target accent was set to about 40k. Table 6 summarizes the results. When more target domain data were used to train the DANN model, the model's performance was significantly improved for most accents. Among the four accent types, the IN accent demonstrated the most significant improvement, with a WER reduction of approximately 3%. Although Table 6 shows that the amount of target domain data affected the accuracy of DANN, it is difficult to say that the experiments perfectly observe the tendency, due to the limited amount of data for some accents. The final experiment focused on investigating the effect of the amount of target domain data in detail. This experiment was conducted using only the speech files of EN, which is the accent with the largest amount of target domain data among the four target accents. Five models were constructed, ranging from DANN-EN1 to DANN-EN5, while varying the amount of EN accent data (20k, 40k, 60k, 80k, and 100k for EN1, EN2, EN3, EN4, and EN5, respectively). In all the DANN-EN models, the amount of source domain (US) data was 160k. For example, DANN-EN3 was trained with US-160k and EN-60k data. Table 7 presents the performance of the five models. As the amount of target domain data increased, the accuracy of accented speech recognition consistently improved. In particular, DANN-EN5 achieved a WER reduction of about 9% in comparison with DANN-EN1. This result shows the possibility that collecting \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Accent**} & \multicolumn{2}{c}{**Model**} \\ \cline{2-3}  & **DANN** & **DANN-inc** \\ \hline AU & 24.22\%/59.80\% & 22.72\%/57.38\% \\ CA & 13.55\%/40.15\% & 13.63\%/40.02\% \\ EN & 21.60\%/54.67\% & 19.96\%/52.53\% \\ IN & 28.83\%/66.49\% & 26.24\%/63.56\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance (CER/WER) of DANN models by adding more target domain data. a larger amount of target accent data will improve the performance of accented speech recognition, enhancing the correctness of DANN models.