## 5 General Discussion and Conclusions
Although the use of VTLN maintained this trend, it improved recognition performance for younger children for all ages more compared to that of teenagers. For Dutch, the female speech was consistently recognized better than the male speech, in line with previous findings for this database [27]. The application of VTLN gave very similar improvements for both genders in the database. Both speed perturbations and spectral augmentation are often used as data augmentation techniques in E2E and have shown their effectiveness in improving recognition performance for adults' speech [21; 22] despite the fact that both methods can potentially lead to artifacts in the generated speech signal and acoustic features, respectively. Speed perturbation, for instance, alters the speech signal's pitch and speed, which occasionally leads to unnatural or distorted sounds (as shown by a different experiments in our lab). Spectral augmentation modifies spectral characteristics; however, we do not know which spectral information is modified; thus, the model is possibly also learning artificial patterns. In this work, we did not check for these artifacts nor did we try to optimize the parameter settings of these two methods; we used the standard settings. The results shown in this paper, however, indicate that the benefit of applying SP and SpecAug is larger than the negative effect of potential artifacts. Future research could investigate whether further performance benefits could be obtained when the parameter settings are tuned to the task at hand and artifacts are removed. Regarding VTLN: While our study highlighted VTLN's impact across different languages, its applicability and integration in E2E models may encounter challenges. For instance, because VTLN needs to be trained independently and then used as a processing step after feature extraction to warp the features for training the ASR network architecture, it may not be compatible with architectures that utilize raw waveform data rather than features. As a result, integrating VTLN into such architectures requires further exploration. In the future, we intend to explore the performance of existing pre-trained models, such as Whisper, in these languages as an alternative to the baseline model without augmentations or as an alternative to the model trained using data augmentations. By doing so, we aim to investigate whether VTLN still offers additional complementary information when employed with pre-trained models that are already trained on a diverse type and even diverse speaker groups. While retraining these pre-trained models is not always feasible or desirable for computational reasons, using VTLN only during testing could potentially enhance the recognition performance of pre-trained models without extensive retraining, with the ultimate aim to remove bias against children's speech in automatic speech recognition. In conclusion, this research contributes to narrowing the performance gap between children's and adults' speech recognition, especially when children's speech and text data are absent for training. By training our VTLN model on adults' speech and using state-of-the-art speed perturbations and spectral augmentation techniques applied to adults' speech, we improved recognition performance across diverse child speaker groups, speaking styles, and languages, thus showing that these approaches generalize across age, speaking styles, and languages. Performance was further improved when children's speech and/or highly variable speech was used to train the VTLN model.