  Children speech recognition is indispensable but challenging due to the
diversity of children's speech. In this paper, we propose a filter-based
discriminative autoencoder for acoustic modeling. To filter out the influence
of various speaker types and pitches, auxiliary information of the speaker and
pitch features is input into the encoder together with the acoustic features to
generate phonetic embeddings. In the training phase, the decoder uses the
auxiliary information and the phonetic embedding extracted by the encoder to
reconstruct the input acoustic features. The autoencoder is trained by
simultaneously minimizing the ASR loss and feature reconstruction error. The
framework can make the phonetic embedding purer, resulting in more accurate
senone (triphone-state) scores. Evaluated on the test set of the CMU Kids
corpus, our system achieves a 7.8% relative WER reduction compared to the
baseline system. In the domain adaptation experiment, our system also
outperforms the baseline system on the British-accent PF-STAR task.
