  Batch normalization (BN) is an effective method to accelerate model training
and improve the generalization performance of neural networks. In this paper,
we propose an improved batch normalization technique called attentive batch
normalization (ABN) in Long Short Term Memory (LSTM) based acoustic modeling
for automatic speech recognition (ASR). In the proposed method, an auxiliary
network is used to dynamically generate the scaling and shifting parameters in
batch normalization, and attention mechanisms are introduced to improve their
regularized performance. Furthermore, two schemes, frame-level and
utterance-level ABN, are investigated. We evaluate our proposed methods on
Mandarin and Uyghur ASR tasks, respectively. The experimental results show that
the proposed ABN greatly improves the performance of batch normalization in
terms of transcription accuracy for both languages.
