Squeeze for sneeze: Compact neural networks for cold and flu recognition

Merlin Albes, Zhao Ren, Björn W. Schuller, Nicholas Cummins

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

In digital health applications, speech offers advantages over other physiological signals, in that it can be easily collected, transmitted, and stored using mobile and Internet of Things (IoT) technologies. However, to take full advantage of this positioning, speech-based machine learning models need to be deployed on devices that can have considerable memory and power constraints. These constraints are particularly apparent when attempting to deploy deep learning models, as they require substantial amounts of memory and data movement operations. Herein, we test the suitability of pruning and quantisation as two methods to compress the overall size of neural networks trained for a health-driven speech classification task. Key results presented on the Upper Respiratory Tract Infection Corpus indicate that pruning, then quantising a network can reduce the number of operational weights by almost 90 %. They also demonstrate the overall size of the network can be reduced by almost 95 %, as measured in MB, without affecting overall recognition performance.

Original languageEnglish
Title of host publicationInterspeech 2020
PublisherInternational Speech Communication Association
Pages4546-4550
Number of pages5
ISBN (Print)9781713820697
DOIs
StatePublished - 2020
Externally publishedYes
Event21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 - Shanghai, China
Duration: 25 Oct 202029 Oct 2020

Publication series

NameProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2020-October
ISSN (Print)2308-457X
ISSN (Electronic)1990-9772

Conference

Conference21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020
Country/TerritoryChina
CityShanghai
Period25/10/2029/10/20

Keywords

  • Cold
  • Compact Neural Networks
  • Computational Paralinguistics
  • Flu Recognition
  • Pruning
  • Quantisation

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