Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26251
Appears in Collections:Computing Science and Mathematics Journal Articles
Title: Deep and sparse learning in speech and language processing: An overview
Authors: Wang, Dong
Zhou, Qiang
Hussain, Amir
Contact Email: ahu@cs.stir.ac.uk
Keywords: Deep learning
Sparse coding
Speech processing
Language processing
Issue Date: 2016
Citation: Wang D, Zhou Q & Hussain A (2016) Deep and sparse learning in speech and language processing: An overview In: Liu CL, Hussain A, Luo B, Tan KC, Zeng Y, Zhang Z (ed.) Advances in Brain Inspired Cognitive Systems. BICS 2016, Cham, Switzerland: Springer. BICS 2016: 8th International Conference on Brain-Inspired Cognitive Systems, 28.11.2016 - 30.11.2016, Beijing, China, pp. 171-183.
Series/Report no.: Lecture Notes in Computer Science, 10023
Abstract: Large-scale deep neural models, e.g., deep neural networks (DNN) and recurrent neural networks (RNN), have demonstrated significant success in solving various challenging tasks of speech and language processing (SLP), including speech recognition, speech synthesis, document classification and question answering. This growing impact corroborates the neurobiological evidence concerning the presence of layer-wise deep processing in the human brain. On the other hand, sparse coding representation has also gained similar success in SLP, particularly in signal processing, demonstrating sparsity as another important neurobiological characteristic. Recently, research in these two directions is leading to increasing cross-fertlisation of ideas, thus a unified Sparse Deep or Deep Sparse learning framework warrants much attention. This paper aims to provide an overview of growing interest in this unified framework, and also outlines future research possibilities in this multi-disciplinary area.
URL: https://link.springer.com/chapter/10.1007/978-3-319-49685-6_16
DOI Link: http://dx.doi.org/10.1007/978-3-319-49685-6_16
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