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Title:	Compressed Multimodal Hierarchical Extreme Learning Machine for Speech Enhancement
Authors:	廖文宏 Liao, Wen-Hung Hussain, Tassadaq Tsao, Yu Wang, Hsin-Min Wang, Jia-Ching Siniscalchi, Sabato Marco
Contributors:	資科系
Keywords:	Data compression;Feedforward neural networks;Quantization (signal);Speech enhancement
Date:	2019-11
Issue Date:	2021-06-04 14:46:13 (UTC+8)
Abstract:	Recently, model compression that aims to facilitate the use of deep models in real-world applications has attracted considerable attention. Several model compression techniques have been proposed to reduce computational costs without significantly degrading the achievable performance. In this paper, we propose a multimodal framework for speech enhancement (SE) by utilizing a hierarchical extreme learning machine (HELM) to enhance the performance of conventional HELM-based SE frameworks that consider audio information only. Furthermore, we investigate the performance of the HELM-based multimodal SE framework trained using binary weights and quantized input data to reduce the computational requirement. The experimental results show that the proposed multimodal SE framework outperforms the conventional HELM-based SE framework in terms of three standard objective evaluation metrics. The results also show that the performance of the proposed multimodal SE framework is only slightly degraded, when the model is compressed through model binarization and quantized input data.
Relation:	Proceedings of 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE Signal Processing Society
Data Type:	conference
DOI link:	https://doi.org/10.1109/APSIPAASC47483.2019.9023012
DOI:	10.1109/APSIPAASC47483.2019.9023012
Appears in Collections:	[Department of Computer Science ] Proceedings

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