## 4 Results and Analyses
### Experiment 3: The Combined Effect of Augmentation and Normalization  
Until now, we have discussed the effects of augmentation and normalization separately. The results show that overall all approaches outperform the baseline systems on children's speech, where the combination of SP and SpecAug gave the best results (except for Dutch children, DC-Read, where using normalization alone by the VTLN\({}_{\text{Jas-DCDTNnT}}\) model gave the best results). For Experiment 3, all of the approaches (augmentation and normalization) are combined, and we investigate different data sets for training and applying VTLN. **Dutch:** In Table 9 (see Appendix B Table A5 for the \(p\)-values), we present the results of combining SP and SpecAug with different VTLN models when VTLN was applied during training and testing and only during testing. The results are again split for adult read (Rd)and continuous (CTS) speech and for DC, DT, and NnT split for Read and HMI speech. The averages over the three speaker groups are also provided. For easy reference, the system trained on adults' speech with augmentations (SP and SpecAug) but without normalization is provided again (same as Table 6, row c). When applying VTLN during training and testing, we observe that all of the models significantly outperform the no-VTLN model for all three speaker groups for read speech, with VTLN\({}_{\text{MultiVI}}\) giving the best result for DC and DT, with improvements of 5.5% and 1.1%, respectively, and VTLN\({}_{\text{Jas-DCDTNnT}}\) giving the best results for the non-native teenagers, with an improvement of 1.0% WER absolute. VTLN\({}_{\text{MultiVI}}\) gives almost the same results on the non-native teenagers (a difference of only 0.1% absolute). Both these models are also trained on non-native accepted speech, which is likely the reason for their improved results on non-native accented teenager speech. For HMI speech, applying VTLN only led to significant improvements for the native DC. No significant differences compared to baseline were found for the DT and NnT speaker groups. Applying VTLN during training and testing does not improve adults' speech. Several general observations can be made about the results when VTLN is applied only during testing. Only for native children speech did applying VTLN lead to significant improvements over baseline for read and HMI speech, while for the native teenagers and non-native teenagers applying VTLN only during testing led to significant performance degradation compared to the baseline. For the native children, the largest improvement for read speech was 1.4% with VTLN\({}_{\text{MultiVI}}\), and for HMI speech 2.2% with VTLN\({}_{\text{CGN}}\). For adults' speech, applying VTLN during testing led to significant performance degradations for all of the models except VTLN\({}_{\text{CGN}}\). This shows that in scenarios where the ASR system cannot be retrained, normalizing the test data alone using VTLN warping factors can reduce the acoustic mismatch between features of adults' and children's speech and improve children's speech recognition; however, it may degrade performance for older children and adults. **German:** As shown in Table 10 (see Appendix B Table A6 for the \(p\)-values), when VTLN is applied during both training and testing, the two VTLN models for which their training data included children's speech significantly improved over baseline. The largest improvement of 2.0% is observed for the VTLN\({}_{\text{kidstALC}}\) model. Slight though significant performance degradations were found when applying VTLN models to test adults' speech. When VTLN is used only during testing, all of the models show a significant performance improvement for children's speech compared to the no-VTLN condition, with the largest improvement of 2.8% for the VTLN\({}_{\text{CV}}\) model. The VTLN\({}_{\text{MultiVI}}\) model also performed similarly to the VTLN\({}_{\text{CV}}\) model. Perhaps surprisingly, for children's speech,