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Recent Progresses in Deep Learning Based Acoustic Models
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DOI：10.1109/JAS.2017.7510508.png)
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Abstract
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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 (RNNs) and convolutional neural networks (CNNs) 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 (CTC) criterion, and the attention-based sequence-to-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.
Keywords:
Attention model
convolutional neural network (CNN)
connectionist temporal classification (CTC)
deep learning (DL)
long short-term memory (LSTM)
permutation invariant training
speech adaptation
speech processing
speech recognition
speech separation
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