## 4 Results and Analyses  
### Baseline Model Performance  
**Dutch:** The results of the Dutch experiments are presented in Table 6 split out for Dutch adults' speech (CGN), Dutch Children (DC), Dutch Teenagers (DT) and Dutch Non-Native Teenagers (NnT), for read and HMI speech, separately. Moreover, the averages over the three children/teenager speaker groups for both speaking styles are reported. The system trained on Dutch adults' speech without any augmentation or normalization, i.e., the baseline model in Table 6 (row a), achieves a 9.6% WER and 23.9% WER on read and CTS adults' speech on the CGN test sets, respectively. These results compare well to those reported in the literature on the CGN datasets, where [71] obtained a WER of 6.6% and 21.6% on CGN read and CTS speech, respectively, with a TDNNF and RNNLM model, (i.e., with the use of an LM, which we do not use in E2E models). To further evaluate our baseline model, we also trained a TDNN-BiLSTM model with SP and a tri-gram LM, which achieved WERs of 7.0% and 26.4% on CGN read and CTS speech, respectively, which are similar to those of [71] while not using an RNNLM. This indicates that our Dutch conformer E2E model is a strong baseline model. On children's speech, the baseline model shows, as expected, much worse results: an average of almost 40% WER for read speech and 50% for HMI speech, with the best results for DT and the worst for the NnT. On the same database, but with the use of an RNNLM, the authors of [27,71] reported somewhat better results: an average of 27.3% WER for read speech and 31.7% for HMI speech. Our TDNN-BiLSTMmodel obtained a WER of 40.2% for read speech and 47.7% for HMI speech, which is a bit worse than the results by [71]. The difference can be attributed to the lack of an RNNLM for our TDNN-BiLSTM model, which improved the performance of the teenagers speech in [71]. **German:** The results of the German experiments are presented in Table 7. The results are reported for adults' speech from the CV dataset and for children's speech from the kidsTALC dataset. The baseline model, in Table 7 (row a), achieves a WER \(<\) 10% on German adults' speech, showing that this is a fairly strong baseline, similar to prior work using the same dataset, which achieved a WER of 5.8% using a Conformer-RNN-T model with SpecAug augmentation [59]. However, the baseline model performs drastically worse on children's speech. The performance on the kidsTALC dataset is close to 78% WER. The only available results on the kidsTALC dataset used models trained on both kidsTALC and CV [38; 72]. The work in [38] reported a 32.5% PER (WER not reported) when trained with kidsTALC and CV, while [72] reported a 21.5% PER (47.8% WER) with Wav2 Vec fine-tuned on kidsTALC and CV. To further evaluate our baseline model, we trained our conformer model (without LM) on the kidsTALC and CV databases, with SP and SpecAug, which achieved a 5.1% WER on the adult CV speech and 40.6% WER on the kidsTALC development set. Thus, our conformer baseline is a good enough baseline. The performance drop from adult to children's speech can be attributed to the high acoustic variability in children's speech and partly to the difference in the recording conditions of the datasets. **Mandarin:** The results of the Mandarin experiments are shown in Table 8. The results are reported on the adults' speech of Set A, and the two children's speech datasets Set C1 (read) and Set C2 (conversational) and average over both children's speech sets. The baseline model, in Table 8 (row a), obtains a CER of 16% on the read adults' speech and