## 2 Background on Children's Speech Recognition (CSR)
### Brief Overview of Research on Children's Speech Recognition Using Adult-Speech-Based ASRs
Typically, in hybrid systems trained with adults' speech, research focuses on developing acoustic features that capture or reduce acoustic variability in children's speech or make the adult acoustic features more like the features of children's speech, often using feature normalization that normalizes vocal tract differences (i.e.,vocal-tract-related features) or that normalizes source differences (i.e., source-level features). The following are examples of vocal-tract-related features: Work in [4] introduced front-end frequency warping and filtering techniques on digit recognition tasks; others used VTLN-based normalization to reduce acoustic variability in features used for the CSR task [13; 14; 47]. The VTLN features were found to work for both English and Italian [25; 49] (see Table 2, second column for the codes of the languages). At the source-level, pitch modifications change adults' speech pitch frequency (source) to that of children's speech (target) [15; 54], and formant modification approaches, which aim to reduce the difference in vocal tract resonances (formant frequencies) between adult and child speakers [51], have been found to enhance CSR systems. A second approach that is investigated to improve CSR is to add children's speech data to the training data for training from scratch or fine-tuning, which adapts the adult ASR to the children's speech [50]. However, children's speech data are not always available, making this approach not always feasible. Next, we discuss several approaches used in E2E systems for CSR, as detailed in Table 3. The E2E systems require a substantial amount of children's speech training data for CSR. Hence, the commonly used approach is to acoustically modify adults' speech such that is sounds more like children's speech. For instance, in [18], the authors used various data augmentation techniques, including pitch, speed, tempo, volume perturbation, spectral augmentation, and reverberation, resulting in nearly tenfold more training data. In [53], data augmentation is carried out by synthesizing reading mistakes (simulating word-level repetitions and substitutions) to improve recognition performance of E2E models. In [16], segmental spectrum warping and perturbations in formant energy are introduced to generate a children-like speech spectrum from that of an adult's speech spectrum. Voice conversion by changing the spectral characteristics of adults' speech is also explored to generate child-like speech from adults' speech as in [17; 33]. Secondly, in situations where some children's speech data are available, transfer learning approaches could be used [52] or the acoustic and language models are adapted to the children's speech and text [11]. Moreover, recently, a common trend has been to enhance recognition performance by fine-tuning large self-supervised speech models with target data, e.g., recent work in [55] demonstrated that fine-tuning Whisper on children's speech significantly improved CSR performance for several English language databases, compared to non-fine-tuned Whisper models. However, similar to transfer learning, this approach involves using children's speech, which differs from our scenario where only adults' speech is used. Summarizing Tables 2 and 3 shows that in the scenario that no children's speech data are available for training, for hybrid systems, most research is carried out on English and read speech data, while the more recent research using E2E models focus on a larger variety of languages. Feature adaptation techniques such as VTLN for children's speech are restricted to GMM-HMM and DNN-HMM systems, which give the best performances on (English) read speech in the range of 10-20% WER. Recognition performances with E2E systems on several other languages reach similar performances on read speech and more than 30% WER on spontaneous speech. Current trends in E2E models employ data creation/generation techniques to improve CSR.