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Toward Accurate and Efficient Feature Selection for Speaker Recognition on Wearables

Authors:

Reza Rawassizadeh, and

David KotzAuthors Info & Claims

WearSys '17: Proceedings of the 2017 Workshop on Wearable Systems and Applications

June 2017

Pages 41 - 46

https://doi.org/10.1145/3089351.3089352

Published: 19 June 2017 Publication History

Abstract

Due to the user-interface limitations of wearable devices, voice-based interfaces are becoming more common; speaker recognition may then address the authentication requirements of wearable applications. Wearable devices have small form factor, limited energy budget and limited computational capacity. In this paper, we examine the challenge of computing speaker recognition on small wearable platforms, and specifically, reducing resource use (energy use, response time) by trimming the input through careful feature selections. For our experiments, we analyze four different feature-selection algorithms and three different feature sets for speaker identification and speaker verification. Our results show that Principal Component Analysis (PCA) with frequency-domain features had the highest accuracy, Pearson Correlation (PC) with time-domain features had the lowest energy use, and recursive feature elimination (RFE) with frequency-domain features had the least latency. Our results can guide developers to choose feature sets and configurations for speaker-authentication algorithms on wearable platforms.

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Cited By

Sabbella HWeerakoon DGulati MMisra A(2024)PA2BLO: Low-Power, Personalized Audio Badge2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494427(154-163)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerCom59722.2024.10494427
Hu CMa XMa DDang T(2023)Lightweight and Non-Invasive User Authentication on EarablesProceedings of the 24th International Workshop on Mobile Computing Systems and Applications10.1145/3572864.3580332(36-41)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1145/3572864.3580332
Rawassizadeh RRong Y(2023)ODSearchProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694886:4(1-25)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3569488
Show More Cited By

Index Terms

Toward Accurate and Efficient Feature Selection for Speaker Recognition on Wearables
1. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Software system models
        Feature interaction

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Published In

cover image ACM Conferences

WearSys '17: Proceedings of the 2017 Workshop on Wearable Systems and Applications

June 2017

60 pages

ISBN:9781450349598

DOI:10.1145/3089351

Program Chairs:
Mi Zhang
Michigan State University, USA
,
Ashwin Ashok
Georgia State University, USA

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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SIGOPS: ACM Special Interest Group on Operating Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 June 2017

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Research-article

Funding Sources

National Science Foundation

Conference

MobiSys'17

Sponsor:

SIGMOBILE

MobiSys'17: The 15th Annual International Conference on Mobile Systems, Applications, and Services

June 19, 2017

New York, Niagara Falls, USA

Acceptance Rates

WearSys '17 Paper Acceptance Rate 9 of 9 submissions, 100%;

Overall Acceptance Rate 28 of 36 submissions, 78%

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Cited By

Sabbella HWeerakoon DGulati MMisra A(2024)PA2BLO: Low-Power, Personalized Audio Badge2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494427(154-163)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerCom59722.2024.10494427
Hu CMa XMa DDang T(2023)Lightweight and Non-Invasive User Authentication on EarablesProceedings of the 24th International Workshop on Mobile Computing Systems and Applications10.1145/3572864.3580332(36-41)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1145/3572864.3580332
Rawassizadeh RRong Y(2023)ODSearchProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694886:4(1-25)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3569488
Nammous MSaeed KKobojek P(2022)Using a small amount of text-independent speech data for a BiLSTM large-scale speaker identification approachJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2020.03.01134:3(764-770)Online publication date: Mar-2022
https://doi.org/10.1016/j.jksuci.2020.03.011
Liu RCornelius CRawassizadeh RPeterson RKotz D(2018)Vocal ResonanceProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31917512:1(1-23)Online publication date: 26-Mar-2018
https://dl.acm.org/doi/10.1145/3191751

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