Deep and sparse learning in speech and language processing: An overview

Wang, Dong; Zhou, Qiang; Hussain, Amir

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.

Citation

Wang, D., Zhou, Q., & Hussain, A. (2016, November). Deep and sparse learning in speech and language processing: An overview. Presented at BICS 2016: International Conference on Brain Inspired Cognitive Systems, Beijing, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: BICS 2016: International Conference on Brain Inspired Cognitive Systems
• Start Date: Nov 28, 2016
• End Date: Nov 30, 2016
• Online Publication Date: Nov 13, 2016
• Publication Date: 2016
• Deposit Date: Oct 4, 2019
• Publisher: Springer
• Pages: 171-183
• Series Title: Lecture Notes in Computer Science
• Series Number: 10023
• Series ISSN: 0302-9743
• Book Title: Advances in Brain Inspired Cognitive Systems
• ISBN: 978-3-319-49684-9
• DOI: https://doi.org/10.1007/978-3-319-49685-6_16
• Keywords: Deep learning; Sparse coding; Speech processing; Language processing
• Public URL: http://researchrepository.napier.ac.uk/Output/1792683