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  1. Proceedings of the 2017 Workshop on Wearable Systems and Applications (WearSys '17)
  2. Toward Accurate and Efficient Feature Selection for Speaker Recognition on Wearables
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Toward Accurate and Efficient Feature Selection for Speaker Recognition on Wearables

Content Provider ACM Digital Library
Author Rawassizadeh, Reza Liu, Rui Kotz, David
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.
Starting Page 41
Ending Page 46
Page Count 6
File Format PDF
ISBN 9781450349598
DOI 10.1145/3089351.3089352
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-06-19
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Feature selection Audio signal processing
Content Type Text
Resource Type Article
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