## 2 Background on Children's Speech Recognition (CSR)
### Brief Overview of Research on Children's Speech Recognition Using Adult-Speech-Based ASRs  
Given the limited availability of children's speech data, many studies employ ASR models trained on and for adults' speech to recognize children's speech, as illustrated in Figure 1 (where solid lines indicate training and dashed lines indicate testing). Although using ASR models trained on adults' speech for the recognition of adults' speech in many cases leads to reasonably good results, using these for children's speech recognition results in sub-optimal recognition performance due to the acoustic and linguistic differences in adults' and children's speech (Figure 1a). \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Databases** & **Language** & **Type of Speech** & **Age Range** & **\#Spk** \\ \hline CID, 1996 [39] & en & Read speech & 5–18 years & 436 \\ SAIL-Inhouse, 1997 [3] & en & Digit, commands and phrases & 5–18 years+ Adult & - \\ CMU Kids Corpus, 1997 [29] & en & Read speech & 6–11 years & 76 \\ CHIMP, 1998 [45] & en & Read speech & 6–14 years & 97 \\ OGI Kids Corpus, 2000 [30] & en & Spontaneous speech & KG to 10 th Grade & 1100 \\ TBALL, 2005 [46] & en, es & Read speech & 5–8 years & 256 \\ PF STAR, 2005 [34] & en, de, sw, it & Read and spontaneous (native + non-native) & 4–15 years & 158 \\ FBR ChildtL, 2005 [35] & it & Read speech & 7–13 years & 170 \\ CU Read, 2006 [31] & en & Isolated words and sentences & 6–11 years (1–5 Grade) & 663 \\ CU Story, 2006 [32] & en & Read and summarized stories & 8–11 years (3–5 Grade) & 106 \\ CGN-Jasmin, 2006 [36] & nl & Read and machine interaction speech (native+non-native) & 6–18 years & 190 \\ My Science Tutor (MyST), 2011 [40] & en & Children and virtual tutor conversations & 7–11 years (3–4 Grade) & 1370 \\ TLT-school, 2020 [41] & en, de & Spontaneous speech: Non-native & 9–16 years & 10k \\ SLT CSR Challenge, 2021 [37] & zh & Read and conversational & 4–11 years + Adult & 980 \\ kidsTALC, 2022 [38] & de & Continuous speech & 3.5–11 years & 47 \\ \hline \hline \end{tabular}
\begin{tabular}{l l l l} \hline \hline  & KG = Kindergarten, Languages: ISO 639-1 language codes: English = en, Spanish = es, German = de, Swedish = sw, Italian = it, Chinese = zh, and Dutch = nl.  
\end{table} 
Table 1: Common children’s speech databases with details of the language, type of speech, age range, and number of speakers (#Spk). Figure 1: (**a**) ASR model trained on adults’ speech and tested on adults’ and children’s speech; (**b**) modification of adults’ speech or features during the training phase to make the adults’ speech more children’s speech-like (solid orange lines); (**c**) modification of the children’s speech or features during the test phase to make the children’s speech more adults’ speech-like (green dashed block). Solid lines indicate training and dashed lines indicate testing. Approach (**b**,**c**) can also be combined, like in this paper. To improve children's speech recognition, various approaches have been explored to adapt adult speech-based systems. These approaches can be categorized into two main strategies, indicated in Figure 1b,c. Firstly, Figure 1b: Adaptation of _adult training_ data: adults' speech is perturbed, e.g., its speed, duration, or pitch is acoustically modified to resemble children's speech, or the spectral features are perturbed through spectral feature modifications [16], adaptation techniques [4], speaker adaptive training [13], or normalization techniques [12]. The modified adult data is then added to the pre-existing adults' speech, and the ASR system is retrained and used for testing of the adults' and children's speech (green, dashed lines). Secondly, Figure 1c: Modification of children's speech or acoustic features _during test_: The ASR is only trained on adults' speech, as in Figure 1a. However, the children's speech is modified prior to testing to match the characteristics of the adults' speech or features used in training.