  We present a bidirectional unsupervised model pre-training (UPT) method and
apply it to children's automatic speech recognition (ASR). An obstacle to
improving child ASR is the scarcity of child speech databases. A common
approach to alleviate this problem is model pre-training using data from adult
speech. Pre-training can be done using supervised (SPT) or unsupervised
methods, depending on the availability of annotations. Typically, SPT performs
better. In this paper, we focus on UPT to address the situations when
pre-training data are unlabeled. Autoregressive predictive coding (APC), a UPT
method, predicts frames from only one direction, limiting its use to
uni-directional pre-training. Conventional bidirectional UPT methods, however,
predict only a small portion of frames. To extend the benefits of APC to
bi-directional pre-training, Bi-APC is proposed. We then use adaptation
techniques to transfer knowledge learned from adult speech (using the
Librispeech corpus) to child speech (OGI Kids corpus). LSTM-based hybrid
systems are investigated. For the uni-LSTM structure, APC obtains similar WER
improvements to SPT over the baseline. When applied to BLSTM, however, APC is
not as competitive as SPT, but our proposed Bi-APC has comparable improvements
to SPT.
