  The performance of child speech recognition is generally less satisfactory
compared to adult speech due to limited amount of training data. Significant
performance degradation is expected when applying an automatic speech
recognition (ASR) system trained on adult speech to child speech directly, as a
result of domain mismatch. The present study is focused on adult-to-child
acoustic feature conversion to alleviate this mismatch. Different acoustic
feature conversion approaches, including deep neural network based and signal
processing based, are investigated and compared under a fair experimental
setting, in which converted acoustic features from the same amount of labeled
adult speech are used to train the ASR models from scratch. Experimental
results reveal that not all of the conversion methods lead to ASR performance
gain. Specifically, as a classic unsupervised domain adaptation method, the
statistic matching does not show an effectiveness. A disentanglement-based
auto-encoder (DAE) conversion framework is found to be useful and the approach
of F0 normalization achieves the best performance. It is noted that the F0
distribution of converted features is an important attribute to reflect the
conversion quality, while utilizing an adult-child deep classification model to
make judgment is shown to be inappropriate.
