Document Type
Conference Paper
Publication Date
6-19-2017
Publication Title
Proceedings of the ACM Workshop on Wearable Systems and Applications (WearSys)
Department
Department of Computer Science
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.
DOI
10.1145/3089351.3089352
Original Citation
Rui Liu, Reza Rawassizadeh, and David Kotz. Toward Accurate and Efficient Feature Selection for Speaker Recognition on Wearables. In Proceedings of the ACM Workshop on Wearable Systems and Applications (WearSys), June 2017. 10.1145/3089351.3089352
Dartmouth Digital Commons Citation
Liu, Rui; Rawassizadeh, Reza; and Kotz, David, "Toward Accurate and Efficient Feature Selection for Speaker Recognition on Wearables" (2017). Dartmouth Scholarship. 3046.
https://digitalcommons.dartmouth.edu/facoa/3046
