## 6 Discussion  
Achieving high accuracy in recognition of children's speech is difficult using state-of-the-art ASR systems because of two types of mismatch between the system training and testing. First, children typically use ASR applications in noisy environments such as when playing games and when taking part in education with other children. Therefore, when the system is trained with clean speech, there is mismatch between the testing and training stages. Second, due to practical problems in recording young child speakers, few training data are available from child speakers, and current ASR systems are mostly trained using adults' speech only. Therefore, when these ASR systems are used to recognize children' speech, another mismatch will be brought about between the system training and testing stages. The severity of these two mismatches was first demonstrated in the current study using a standard ASR system: a poor WER value (of about 20%) was obtained in recognizing clean children's speech using the system trained using adults' speech. Furthermore, when the children's speech was contaminated with noise in order to simulate the use of children's ASR in realistic environments, the recognition performance deteriorated severely to WER values larger than 80% in some noise conditions. In order to tackle the effects caused by the two mismatches described above in recognition of children's speech in noisy conditions, the current study investigated the utilization of data augmentation and time-scale modification. Furthermore, the time-scale modification technique consisted of two parts, modification of \(F_{0}\) and modification of speaking rate, which were used to convert the prosodic structure of the children's speech test data to become closer to that of the adults' speech used in the system training. The experiments of the study were planned in order to first investigate how the recognition performance was affected when each of the modification techniques was utilized alone in building the recognizer, after which all the studied techniques were combined aiming at the best system. The experiments were conducted using an existing deep neural network (DNN)-based recognizer. Although CNNs are currently increasingly used in ASR, they call for larger numbers of training data compared to DNN-based systems. Therefore, choosing a DNN-based architecture was justified for the current investigation, and we leave the verification of the studied approach with big and complex systems as future work. The data augmentation involved corrupting the original clean training data of adults' speech using additive noise of different types and SNR categories. Three augmentation scenarios (\"same\", \"different\", \"all\") were generated, and these scenarios differ in the way the noise type in testing is seen by the data augmentation procedure. The recognition experiments indicated that data augmentation yielded a consistent improvement in WER only in the case when the noise type was the same in the augmentation and testing. However, when the noise type in testing was different from that used in data augmentation, the performance compared to the baseline decreased considerably, and this happened in all the noise conditions studied. Hence, the utilization of the straightforward data augmentation approach based on noise-corrupting the adult speech in the system training stage did not give an adequate improvement in recognition of noisy children's speech. As the next steps, ASR experiments were conducted by time-scale modifying the prosodic structure of the children's speech in the test stage. The experiments showed that both \(F_{0}\) modification and speaking rate modification improved WER values compared to the baseline system and that this happened for all the noise conditions studied. From the two modification methods, \(F_{0}\) modification yielded smaller WER values in all noise conditions.