# Accented Speech Recognition Based on End-to-End Domain Adversarial Training of Neural Networks
## 4 Experiments and Results
### Experimental Setups  
#### 4.1.1 Speech Corpus  
In this study, all experiments were conducted using the Common Voice corpus [19], which is an open-source speech database released by Mozilla in 2019. As this corpus consists of a significant amount of well-refined speech data, it is widely used for various speech recognition tasks [20]. In particular, this corpus can be effectively utilized in the field of accented speech recognition because speaker information such as age, gender, language, and accent type are provided. The Common Voice corpus has speech data for 60 languages ranging from widely used languages such as English and Spanish to relatively unfamiliar languages such as Basque and Welsh. This study targeted English accents, using speech data from five English accents including US (US accent), AU (Australian accent), CA (Canadian accent), EN (British English (England) accent) and IN (Indian accent). Hereafter, the accent names are denoted by their respective abbreviations for convenience. For domain adaptation, US was determined as the source domain, while the other four accents were regarded as the target domains, because the quantity of US data is much larger than that of other accents. All the speech files were pre-processed for the experiments. The downloaded speech files (.mp3) were converted into wav format and sampled at 16,000 Hz. The files were then divided into the training set (Table 2), the validation set (Table 3), and the test set (Table 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Region** & **Number of Files** & **Hours** \\ \hline AU-test & Australia & 2000 & 3 \\ CA-test & Canada & 2000 & 3 \\ EN-test & England & 2000 & 3 \\ IN-test & India & 2000 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of the test set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Region** & **Number of Files** & **Hours** \\ \hline US-160k & US & 160,000 & 196 \\ AU-20k & Australia & 20,000 & 26 \\ CA-20k & Canada & 20,000 & 25 \\ CA-32k & Canada & 32,000 & 43 \\ EN-20k & England & 20,000 & 24 \\ EN-40k & England & 40,000 & 53 \\ EN-60k & England & 60,000 & 77 \\ EN-80k & England & 80,000 & 101 \\ EN-100k & England & 100,000 & 126 \\ IN-20k & India & 20,000 & 26 \\ IN-40k & India & 40,000 & 57 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the training set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Region** & **Number of Files** & **Hours** \\ \hline AU-val & Australia & 2000 & 3 \\ CA-val & Canada & 2000 & 3 \\ EN-val & England & 2000 & 3 \\ IN-val & India & 2000 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the validation set.  
There were no duplicated files in these three sets. The training set was used to train the ASR model. The names of the datasets in Table 2 indicate the accent type and the number of files. For example, AU-20k indicates an Australian accent dataset with 20,000 files. The validation set was used to calibrate the hyperparameters of the ASR model. It played a role in enhancing the credibility of the experimental results. To obtain reliable experimental results, the hyperparameters needed to be calibrated in detail using the validation set. Finally, the ASR model was evaluated using the test set.