  Automatic speech recognition (ASR) systems for young children are needed due
to the importance of age-appropriate educational technology. Because of the
lack of publicly available young child speech data, feature extraction
strategies such as feature normalization and data augmentation must be
considered to successfully train child ASR systems. This study proposes a novel
technique for child ASR using both feature normalization and data augmentation
methods based on the relationship between formants and fundamental frequency
($f_o$). Both the $f_o$ feature normalization and data augmentation techniques
are implemented as a frequency shift in the Mel domain. These techniques are
evaluated on a child read speech ASR task. Child ASR systems are trained by
adapting a BLSTM-based acoustic model trained on adult speech. Using both $f_o$
normalization and data augmentation results in a relative word error rate (WER)
improvement of 19.3% over the baseline when tested on the OGI Kids' Speech
Corpus, and the resulting child ASR system achieves the best WER currently
reported on this corpus.
