###### Abstract
There are a few existing ASR studies that have addressed both the mismatch induced by having clean speech in training and noisy speech in testing as well as by having adult speech in training vs. child speech in testing. These research questions were studied, for example, in [21] by investigating the use of spectral moments and spectral smoothing-based features, and the results showed that the spectral moment time-frequency distribution augmented by low-order cepstral (SMAC) features were found to improve the recognition performance. In [22], variational mode decomposition (VMD)-based spectral smoothing was found to improve ASR for children's speech in noise. In [23; 24; 25; 26], the feature-space maximum likelihood linear regression (fMLLR) transform, deep convolutional neural networks (CNNs), graph-based feature filtering, and Bayes methods were investigated to address the problems caused by different channel and noise conditions. The effect of feature learning using a CNN-based end-to-end acoustic modeling approach was studied in [27], and the method was shown to give a reduction in WER. Principal component analysis and heteroscedastic linear discriminant analysis based on a low-rank feature projection were explored in [28]. In [29], prosodic features including loudness, intensity, and voice probability were investigated, and it was found that combining the prosodic features with mel-frequency cepstral coefficients (MFCCs) improved ASR performance. The effect of the filter bank on ASR of children's speech was studied in [30], and it was found that the linear-frequency filter bank was better compared to the mel and inverse-mel filter banks. The effects of acoustic and linguistic differences between adults' and children's speech have been investigated widely [22; 29; 31; 32], and these differences have been observed to degrade ASR performance considerably. Modifying prosodic features (\(F_{0}\) and speaking rate) was found to reduce the effect of mismatch induced by having adults' (clean) speech in training and children's (clean) speech in testing in [33; 34; 35]. The effect of modifying formants in ASR of children's speech in clean and noisy conditions was explored recently in [36], and the results showed a reduction in WER when formants of children's speech were modified towards those in adults' speech. The performance of the formant modification method proposed in [36] is, however, limited due to the use of all-pole spectral modeling methods whose accuracy deteriorates in low SNR levels below 5 dB. In [37], generative adversarial network (GAN)-based data augmentation was explored, and an improvement in WER was reported. A data augmentation strategy based on modifying prosody (\(F_{0}\) and speaking rate) by changing glottal closure instants was studied in [38]. In [18], data augmentation using stochastic feature mapping (SFM) to transform out-of-domain adult data was found to improve recognition of children's speech. This study investigates ASR of children's speech by focusing on two challenges: (1) recognition of speech in noisy conditions, which is a typical scenario for child users, and (2) recognition of children's speech using an ASR system trained with adult speech due to the lack of training data from child speakers. In order to address these two challenges, the study combines data augmentation and time-scale modification. In the former, a straightforward data augmentation method is used by corrupting the training data of the ASR system with additive noise to obtain new speech data that correspond better with the testing data in noisy conditions. In the latter, two modification methods (\(F_{0}\) modification and speaking rate modification) are used to modify the prosodic characteristics of the children's speech in the testing phase towards the prosodic characteristics of the adult speech that is used in the system training phase.