Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80465
Title: A robust multilevel speech verification wavelet decomposition for inadequate training data sets of mobile device systems
Authors: Tseng, KK
Zhang, Y
Yung, KL 
Ip, WH 
Ou, ZY
Na, Q
Keywords: Biometric
Speech verification
Wavelet transform
Mobile computing
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2019, v. 7, p. 2418-2428 How to cite?
Journal: IEEE access 
Abstract: With the development of speech signal processing, universality, easy collection and personal speech signal uniqueness, many researchers are attracted to the field of speech verification. Most of the current speech verifications are based on long training data sets in order to achieve good results, and there are no good verification schemes in case of inadequate training data sets. This paper proposes a novel architecture for speech verification using a multilevel method, which extracts feature parameters through a multiple wavelet transform for mobile phone voice. The experiments show that the multilevel wavelet authentication architecture improves performance in speech verification. The recognition rate of the mobile phone system is more robust and superior to other methods.
URI: http://hdl.handle.net/10397/80465
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2018.2886428
Rights: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
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The following publication Tseng, K. K., Zhang, Y., Yung, K. L., Ip, W. H., Ou, Z. Y., & Na, Q. (2019). A robust multilevel speech verification wavelet decomposition for inadequate training data sets of mobile device systems. IEEE Access, 7, 2418-2428 is available at https://dx.doi.org/10.1109/ACCESS.2018.2886428
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