## 5 General Discussion and Conclusions
To assess the impact of database mismatch, we tested the SLT Mandarin SP + SpecAug model (without LM) on three other Mandarin read speech databases: Magic data [75] (43 k utterances, 52 h, 78 speakers), Aishell [76] (7 k utterances, 10 h, 20 speakers), and THCHS-30 [77] (2 k utterances, 6.3 h, 10 speakers), and we obtained 2.48%, 4.10%, and 14.25% CER, respectively. The results for Magic data and Aishell are good despite database mismatch, suggesting that the relatively easy recognition task of read speech may counterbalance the effect of database mismatch, which is in line with the results for our adults' and children's Mandarin read speech. However, THCHS-30, which consists of longer utterances, shows a drop in performance. This shows that the impact of database mismatch (also) depends on the specific database characteristics. Given that for both Dutch and German the children's speech is partially or entirely non-read speech, the observed drop in recognition performance from adult to children's speech is only partially explained by the database mismatch and is thus also due to the acoustic differences between adults' and children's speech. Similar to what has been observed before in a Mandarin E2E system for children's speech in literature [18], applying speed perturbations reduced the WERs for children's speech recognition. Performance was further improved when SpecAug was added. The beneficial effect of SpecAug is in line with findings for English children's speech, which showed an improvement when applying SpecAug over a condition without SpecAug [78]. Our results confirm and extend these earlier findings with a few observations: we observed improvements when using speed-perturbed adults' speech for children's speech recognition. We attribute this to the pitch and speed changes caused by the speed perturbations, which make the adults' speech more similar to children's speech. However, this positive effect of adding perturbed adults' speech was only observed for native speakers and was absent for Dutch non-native speakers. Applying SpecAug led to performance improvements for all speaker groups, with a more substantial impact on non-read speech types. This emphasizes that augmentation techniques may not always and uniformly enhance performance but rather depend on specific characteristics of the speaker group and speech type, which is in line with findings from [23]. As far as we are aware, we are the first to apply VTLN to adults' speech for the improvement of children's speech recognition in E2E models. Our results showed that the application of VTLN improved children's speech recognition across the board both when applying models trained on adults' speech only and when trained on children's speech (from the same database as the test data) only; however, the improvement was smaller than for the combined SP and SpecAug data augmentation methods. The combination of SP, SpecAug, and VTLN, however, gave the best children's speech recognition results for all three languages. Similar to what has been found for hybrid models [50], VTLN, even when trained on adults' speech only, thus also improves the recognition performance of children's speech in the absence of children's speech training data in E2E models without any language model. This result not only shows that VTLN provides a complementary approach and improvement to data augmentation but also that the same approach can be used across languages to improve children's speech recognition. Moreover, since we tested different types of speech (read, HMI, and spontaneous speech), these results show that the combined approach also generalizes over speech styles. Importantly, the performance on adults' speech was maintained. Thus, reducing spectral variation resulting from vocal tract length differences, which are particularly relevant to children's speech, does not impact performance on adults' speech recognition.