## 2 Background on Children's Speech Recognition (CSR)
### Brief Overview of Research on Children's Speech Recognition Using Adult-Speech-Based ASRs
Until recently, hybrid Acoustic Model and Language Model (AM-LM) systems dominated CSR applications and focused on capturing the acoustic variability in children's speech through improved acoustic features [15; 47]. More recently, E2E models have been proposed for CSR applications in which, typically, adults' speech training data is acoustically modified to resemble children's speech using augmentation or conversion techniques [17; 48]. In the remainder of this section, we present a brief (non-exhaustive) overview of research on CSR covering the commonly used approaches where perturbed adults' speech is used for training, first for hybrid systems and then for E2E systems. Note that some of the techniques included in this overview assume the availability of children's speech for training/fine-tuning (unlike the scenario in our research). We describe the databases and languages used; the models and techniques used, along with the training and test details; and the improvements obtained with the applied technique, focusing on the choice of training speaker groups (adult and/or child). This analysis helps us identify gaps and informs our approach to enhance children's speech recognition. An overview of the papers that use hybrid models is presented in Tables 2 and 3 for the E2E models. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Literature** & **CorpusLang** & **Techniques** & **Training Test Details** & **Model** & **Metric Improvement** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{l} [PERSON] et al., 2003 [4] \\ \end{tabular} } & \multirow{2}{*}{SAIL-own: en} & Frequency warping, model adaptation & Adult1 Children & GMM & WER 15.9\% to 8.7\% \\  & & & Adult + Children Children & GMM & WER 7.6\% to 5.6\% \\ \cline{2-5}  & & & Adult 1 Children & GMM & WER 11.3\% to 2.15\% \\ \cline{2-5}  & & & Adult & Female 1 Children & GMM & WER 28.7\% to 18.0\% \\ \cline{2-5}  & & & Adult + Children & GMM & WER 17.4\% to 12.3\% \\ \cline{2-5}  & & & Adult & WSFCAM 01P\_STAR & DNN & WER 24.2\% to 21.4\% \\ \cline{2-5}  & & & Loudness, voice-intensity, and voice-probability & Adult WSFCAM01P\_STAR & DNN & WER 19.6\% to 12.7\% \\ \cline{2-5}  & & & Adult + Children & DNN & WER 11.4\% to 8.8\% \\ \cline{2-5}  & & & Adult & TEDIUM-UCM Kid’s, OCI, CHIMP’CID & DNN & WER 39.3\% to 7.7\% \\ \cline{2-5}  & & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \end{tabular}
\end{table} 
Table 2: Summary of CSR research using hybrid ASR systems: databases details, techniques, and models used. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Literature** & **CorpusLang** & **Techniques** & **Training Test Details** & **Model** & **Metric Improvement** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{l} [PERSON] et al., 2020 [18] \\ \end{tabular} } & \multirow{2}{*}{SLT Challenge: zh} & \multirow{2}{*}{Fikh, speech, tempo, volume, and reverberation} & Adult + Children & E2E & CER-16.2\% to 13.6\% \\  & & & Transfer Learning (base on Adult) & Adult \\ \cline{2-5}  & & SpecAugment, RIR, and volume perturbation & Adult + Child & E2E-LM & CER-38.5\% to 22.6\% \\ \cline{2-5}  & & & Adult + Child & E2E-LM & CER-23.6\% to 20.1\% \\ \cline{2-5}  & & & Adult + Common Voice +’Laido’ & E2E & PER 22.9\% to 19.9\% \\ \cline{2-5}  & & & Librispeech + 'library’ & WSF & E2E-LM & WER 25.4\% to 16.0\% \\ \cline{2-5}  & & & Librispeech Adult + 'Lalido’ & E2E & WER-36.1\% to 32.2\% \\ \cline{2-5}  & & & Adult + Child & E2E & WER-51.3\% to 36.3\% \\ \cline{2-5}  & & & Adult + Child & E2E & WER-28.7\% to 26.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of CSR research using E2E ASR systems: databases details, techniques, and models used. Hybrid systems generally use Gaussian Mixture Models (GMM) or Deep Neural Network (DNN)-based AM-LM and are typically evaluated using Word Error Rate (WER) as the evaluation metric (see also Table 2).