annotated children's speech data makes it a low-resource problem. These issues make the research and development of Children's Speech Recognition (CSR) systems challenging. In this study, our main aim is to improve the performance of Automatic Speech Recognition (ASR) systems for children's speech in the absence of any children's training data (speech and text)--a situation occurring for many languages in the world and leading to large performance gaps between adults' and children's speech recognition performance. For instance, the authors of [11] showed that an End-to-End (E2E) transformer-based ASR System (without a Language Model (LM)) trained on English adults' speech of Librispeech achieved a 2.89% Word Error Rate (WER) for adults, but the WER increased to 38.8% on the MyST corpus and reached 87.2% on the OGI Kids Corpus. These high error rates are dependent on different factors, including age and speech types, and are likely to be similar for other languages. We aim to improve children's speech recognition performance by tackling the two biggest problems outlined above: the mismatch between adults' and children's speech, i.e., the variability in children's speech on one hand, and data scarcity on the other hand. The mismatch between adults' and children's speech and variability in children's speech is often addressed by capturing the acoustic variability in children's speech through improved acoustic features (see also Section 2.2). For example, Vocal Tract Length Normalization (VTLN) [12] has been widely used to reduce the acoustic feature mismatch between adults' and children's speech due to vocal tract length variations [13; 14]. Children typically have a shorter vocal tract length, resulting in higher-frequency sounds; the VTLN technique normalizes speech features based on the estimated warping factors, which account for the variations in vocal tract length. To tackle the data scarcity issue, typically, adults' speech training data are acoustically modified to resemble children's speech (see also Section 2.2), for instance, through pitch modification [15], spectral modification [16], voice conversion [17], and speed perturbations (SP) [18], and the additional, modified speech is used as (additional) training material. The chosen scenario, that no child data are available, also means that no LM will be used. Note that although LM integration could enhance performance, especially in cases of read speech by adult speakers [19], it might not effectively model the unique patterns found in children's speech as the grammar and structure of children's speech is different from that of adults. This was also observed in [11], where it was observed that an E2E transformer-based ASR trained on the adults' speech from Librispeech, without an LM, outperformed an E2E ASR with an LM incorporated when tested on children's speech from the MyST corpus and OGI Kids corpus. In this study, we focus on E2E models for the recognition performance advantages they provide over hybrid models [20] and investigate well-known approaches in hybrid modeling for their potential in E2E children speech recognition. We compare the effectiveness of VTLN and two specifically chosen data augmentation techniques: Speed Perturbations (SP) [21] and Spectral Augmentation (SpecAug) [22]. Speed perturbation is chosen as it allows for mimicking a child's higher pitch and slower speaking rate compared to adults: increasing the speed rate of adults' speech increases the pitch and lowering the speed mimics the slower speaking style of children. Speed perturbation might thus mimic parts of children's speech. Spectral augmentation was chosen as it has often been found to make the ASR system more robust to non-read speech [23].