## 5 General Discussion and Conclusions  
In this study, we investigated data augmentation (speed perturbations and spectral augmentation) and feature normalization techniques (vocal tract length normalization) for E2E children's speech recognition in the scenario that there are no children's speech and text data available for (re)training the ASR system. We investigated the effect of these three approaches in isolation and together across three different languages, different speaking styles, and different children/teenager age groups and compared the results to those for adults' speech. For these languages, the baseline ASR models trained on adults' speech achieved a WER of \(<\)10% on adults' speech and were found to be close to or even better than state-of-the-art results for the respective data sets/languages, and they outperformed the state-of-the-art OpenAI-Whisper small and medium models (and even the large models for Dutch and Mandarin). However, performance deteriorated substantially when tested on children's speech. For Dutch, a drop of 30% absolute was observed for read speech and of over 40% absolute for the more spontaneous human-machine interaction speech. For German continuous speech, the deterioration was close to 70% absolute. For Mandarin, the picture was slightly different. Here, there was no performance drop from adult read speech to children's read speech; however, a drop of around 16% absolute (around 50% relative) \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**DC-Read**} & \multicolumn{2}{c}{**DT-Read**} & \multicolumn{2}{c}{**NnT-Read**} & \multicolumn{2}{c}{**DC-HMI**} & \multicolumn{2}{c}{**DT-HMI**} & \multicolumn{2}{c}{**NnT-HMI**} \\ \hline
**Normalization** & **Female** & **Male** & **Female** & **Male** & **Female** & **Male** & **Female** & **Male** & **Female** & **Male** & **Female** & **Male** \\ \hline
**None** & 36.26 & 35.92 & 17.18 & 20.39 & 50.32 & 52.01 & 40.67 & 39.44 & **26.38** & **29.09** & 50.56 & **54.59** \\
**Train I Test** & **30.94** & **32.17** & **15.61** & **19.8** & **49.49** & **50.82** & **37.44** & **38.48** & 26.81 & 31.46 & 50.67 & 54.75 \\
**Test** & 33.77 & 35.59 & 16.95 & 21.18 & 50.94 & 53.14 & 37.71 & 38.73 & 26.69 & 29.95 & **50.42** & 56.06 \\ \hline Average & 33.66 & 34.56 & 16.58 & 20.46 & 50.25 & 51.99 & 38.61 & 38.88 & 26.63 & 30.17 & 50.55 & 55.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Average WER for male and female speakers of the Dutch Jasmin test sets for different speaker groups (DC, DT, and NnT) and different speech types (Read, HMI) for the model with SP and SpecAug without any normalization (None), with VTLN\({}_{\text{MultiV1}}\) normalization both while training and testing (Train I Test), and VTLN\({}_{\text{MultiV1}}\) normalization only while testing (Test). The lowest WERs for each speaker group are highlighted in bold. Figure 4: Age-wise average WER for female (red lines) and male (blue lines) speakers of the Dutch Jasmin test sets for different speaker groups (DC, DT, and NnT) and different speech types (Read, HMI). Models: ASR model with SP and SpecAug and without any normalization (solid), ASR with VTLN\({}_{\text{MultiV1}}\) normalization both while training and testing (dotted), and ASR with VTLN\({}_{\text{MultiV1}}\) normalization only while testing (dashed). was observed for the children's speech data set, which consisted of spontaneous speech, including that of younger children than in the read speech set. The lack of performance drop from adult to children's read speech for Mandarin is likely at least partially explained by the fact that for Mandarin, the adults' and children's speech sets are part of the same corpus, with the same recording conditions, while for Dutch and German the adults' and children's speech came from separate databases.