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Compressed multimodal hierarchical extreme learning machine for speech enhancement
Tassadaq Hussain
, Yu Tsao
, Hsin Min Wang
,
Jia Ching Wang
, Sabato Marco Siniscalchi
, Wen Hung Liao
資訊工程學系
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Keyphrases
Speech Enhancement
100%
Hierarchical Extreme Learning Machine (H-ELM)
100%
Multimodal Speech
42%
Quantized Input
28%
Model Compression
28%
Real-world Application
14%
Computational Cost
14%
Computational Requirements
14%
Binary Weights
14%
Objective Evaluation
14%
Binarization
14%
Compression Techniques
14%
Achievable Performance
14%
Audio Information
14%
Multimodal Framework
14%
Deep Model
14%
Computer Science
Speech Enhancement
100%
Extreme Learning Machine
100%
Model Compression
28%
Experimental Result
14%
Evaluation Metric
14%
Computational Cost
14%
Compression Technique
14%
World Application
14%
Achievable Performance
14%
binarization
14%
Engineering
Speech Enhancement
100%
Extreme Learning Machine
100%
Input Data
28%
Experimental Result
14%
Metrics
14%
Computational Cost
14%
Compression Technique
14%
Real World Application
14%
Objective Evaluation
14%