## 4 Results and Analyses
### Experiment 1: Augmentation  
**Dutch:** For Dutch, adding speed-perturbed native adults' speech data to the training data Table 6 (row b; see Appendix B, Table A2 for the \(p\)-values) led to a significant performance improvement for native children's and teenager's speech, with absolute improvements ranging from 1.6% (DT-Read) to 6.4% (DC-HMI). However, adding speed-perturbed adults' speech to the training data led to a significant performance degradation for the non-native teenagers for HMI speech. Apparently, adding more native-accented data to the training data increases the bias against non-native accented speech. This can potentially be attributed to the fact that the SP applied to CGN increases the native speech variability but not the non-native variability. Adding SpecAug (row c) led to a significant further reduction in WER for all native speaker groups, and is (as expected) particularly effective in the case of HMI speech, which showed the largest improvements. Unlike the SP-condition, the SP + SpecAug condition led to recognition results for the non-native speakers that significantly outperformed the baseline. Overall, the combined effect of SP + SpecAug gave an absolute average improvement of 7.2% over the children's speech baseline. This improvement for children's speech did not come at the cost of a deterioration for adults' speech. Rather, adding SP + SpecAug to the training data also led to a significant WER improvement of 2.6% on read adults' speech and 3.7% on continuous adults' speech.  
**German:** For German, the effects of adding SP data and SpecAug are similar to those obtained for Dutch. Focusing on the children's speech, Table 7 (rows b, c; see Appendix B Table A3 for the \(p\)-values) shows that adding SP leads to a significant 6.8% absolute improvement for children's speech, while additionally adding SpecAug further increases performance with 3.9% WER. Adding perturbed adults' speech data (SP) along with SpecAug during training also led to an improvement on the adults' speech, with an overall significant improvement of 4.5% WER.  
**Mandarin:** For Mandarin, adding SP as shown in Table 8 (row b; see Appendix B Table A4) improves children's speech recognition for both read (Set C1) and spontaneous (Set C2) speech, with an average improvement of 6.1% CER, although this improvement is not significant. The best performance is again obtained for the combination of SP and SpecAug, leading to an average improvement over the baseline of 9.1%, which significantly outperforms the baseline model for all datasets. The largest absolute improvement for both SP and SP + SpecAug (Table 8, row c) was found for the spontaneous speech in Set C2 (12% vs. 6.1% for Set C1) as compared to the baseline. This is in line with the findings for Dutch,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Training** & **Augmentation** & **Normalization** & **SetA** & **SetC1** & **SetC2** & **Average** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **CER** \\ \end{tabular} } & (a) None & None & 16.4 & 16.1 & 50.8 & 33.5 \\ \cline{2-6}  & (b) SP & None & 11.0 & 11.2 & 43.6 & 27.4 \\  & (c) SP + SpecAug & None & 9.9 * & **10.0 * & **38.8 * & **24.4** \\ \cline{2-6}  & (d) None & \(\text{VTLN}_{\text{SetA}}\) & 16.3 * & 15.5 * & 46.4 * & 31.0 \\  & (e) None & \(\text{VTLN}_{\text{SetC1C2}}\) & 16.7 * & 15.7 * & 46.3 * & 31.0 \\ \hline \hline \end{tabular} \(\dagger\)_p \(<\) 0.05, \(\ddagger\)_p \(<\) 0.01, and *_p \(<\) 0.001.  
\end{table}
Table 8: Results in %CER, with significance levels, for the Mandarin ASR when trained on SLT adult database and tested on adults’ and children’s speech of the SLT database. The lowest CERs for each speaker group across all systems are highlighted in bold.  
where SpecAug is particularly effective in the case of non-read speech compared to read. Also, we observe that adding adult perturbed data with SpecAug results in a significant performance improvement for the adults' speech (Set A) of 6.5% compared to the baseline.