## 4 Results and Analyses
### Experiment 3: The Combined Effect of Augmentation and Normalization
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**CGN**} & \multicolumn{3}{c}{**[PERSON]: Read**} & \multicolumn{3}{c}{**[PERSON]: HMI**} & \multicolumn{3}{c}{**[PERSON]: Avg**} \\ \hline
**Training** & **VTLN** & **VTLN\({}_{\text{model}}\)** & **Rd** & **CTS** & **DC** & **DT** & **NnT** & **DC** & **DT** & **NnT** & **Read** & **HMI** \\ \hline \multirow{6}{*}{CGN adults’ speech} & None & None & **7.0** & **20.2** & 36.1 & 18.8 & 51.1 & 40.1 & 27.8 & 52.6 & 35.3 & 40.2 \\ \cline{2-11}  & \multirow{4}{*}{Train Test} & VTLN\({}_{\text{Jas-DCDT}}\) & 7.3 & **20.2** & 34.0 * & 17.9 * & 50.5 * & **37.5 * & **27.4** & **52.2** & 34.1 & **39.0** \\  & & VTLN\({}_{\text{Jas-DCDT}}\) & 7.6 \(\ddagger\) & **20.2** & 31.7 * & 17.9 * & 50.7 \(\ddagger\) & 38.2 * & 29.2 & 54.7 & 33.4 & 40.7 \\  & & VTLN\({}_{\text{Jas-DCDT}}\) & 7.2 & 20.4 & 32.4 * & 18.1 * & **50.1** * & 39.0 * & 29.7 & 54.0 & 33.5 & 40.9 \\  & & VTLN\({}_{\text{MultiVI}}\) & 7.3 & 20.6 & **31.6** * & **17.2** * & 50.2 * & 38.0 * & 29.2 & 52.7 & **33.2** & 39.9 \\ \cline{2-11}  & \multirow{4}{*}{Test} & VTLN\({}_{\text{CGN}}\) & **7.0** & **20.4** & 35.0 * & 19.2 \(\ddagger\) & **51.8** * & **37.9** * & 29.1 \(\ddagger\) & **52.7** & **35.3** & 39.9 \\  & & VTLN\({}_{\text{Jas-DCDT}}\) & 7.9 * & 21.5 * & 35.3 * & 19.5 * & 52.7 * & 39.0 \(\ddagger\) & 28.8 \(\ddagger\) & 55.1 * & 35.8 & 40.9 \\  & & VTLN\({}_{\text{Jas-DCDT}}\) & 7.9 * & 21.6 * & 35.2 * & 19.4 * & 52.6 * & 38.9 * & 28.9 \(\ddagger\) & 54.8 * & 35.7 & 40.8 \\  & & VTLN\({}_{\text{MultiVI}}\) & 7.7 * & 20.6 \(\ddagger\) & **34.7** * & **19.1** \(\ddagger\) & 52.0 * & 38.2 * & **28.2** & 53.2 & **35.3** & **39.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results in %WER, with significance levels, for the Dutch ASR system trained with SP and SpecAug when VTLN was applied (1) during training and testing or (2) only during test. The lowest WERs for each speaker group are highlighted in bold for the two normalization approaches separately. Underline indicates the best result for the specific speaker group over all conditions. applying VTLN only during testing outperforms the condition where VTLN is applied during training and testing for all of the models (further discussion on this is provided in Section 5). Again, slight but significant degradations were observed when applying VTLN for adults' speech. **Mandarin:** As shown in Table 11 (see Appendix B Table A7 for the \(p\)-values), when VTLN is applied during both training and testing, no significant changes were observed for the children's speech of Set C1, while for Set C2, all three models significantly improved the recognition of the spontaneous children's speech compared to the no-VTLN condition. Similar to Dutch, the best performing VTLN model is VTLNMultiV1, which gave a significant 0.9% improvement over the no-VTLN condition for Set C2. Using VTLN during training and testing did not further improve the results for Set C1, potentially because the recognition results for Set C1 were already quite good, i.e., at the same level as those for the adults' speech in Set A, leaving very little room for additional gain by applying VTLN. Recognition performance did not change much for adults' speech when applying VTLN in addition to SP and SpecAug during training and testing. When VTLN was used only during testing, similar to German, further significant improvements were observed for spontaneous speech in Set C2, with again the best model being VTLNMultiV1 which gave a 1.7% improvement over the no-VTLN condition (further discussion on this is provided in Section 5). However, again no performance differences were observed for the read speech of Set C1, while for Set A a small though significant degradation compared to baseline for the adults' speech was observed.