Recent progresses in deep learning based acoustic models

In this paper, we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques. We first discuss models such as recurrent neural networks U+0028 RNNs U+0029 and convolutional neural networks U+0028 CNNs U+0029 that can effectively exploit variablelength contextual information, and their various combination with other models. We then describe models that are optimized end-to-end and emphasize on feature representations learned jointly with the rest of the system, the connectionist temporal classification U+0028 CTC U+0029 criterion, and the attention-based sequenceto-sequence translation model. We further illustrate robustness issues in speech recognition systems, and discuss acoustic model adaptation, speech enhancement and separation, and robust training strategies. We also cover modeling techniques that lead to more efficient decoding and discuss possible future directions in acoustic model research.