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GMM-Based Speaker Verification System with Hardware MFCC in SoC Design.

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Title: GMM-Based Speaker Verification System with Hardware MFCC in SoC Design.
Authors: Tsai, Tsung-Han1 (AUTHOR) han@ee.ncu.edu.tw, Wang, Chiao-Li1 (AUTHOR)
Source: Multimedia Tools & Applications. Jun2024, Vol. 83 Issue 19, p56991-57010. 20p.
Subjects: Taiwan Semiconductor Manufacturing Co. Ltd., Integrated circuit verification, Gaussian mixture models, Systems on a chip, Markov processes, Feature extraction
Abstract: In recent years, speaker verification has been extensively explored and has significantly improved its effectiveness. It analyzes the voiceprint characteristics of speakers and finds out the differences in voiceprint characteristics between speakers for verification. In this paper, we propose a text-dependent speaker verification system and its hardware implementation of the feature extraction. The proposed speaker verification system includes two phases: enrollment and verification. In the enrollment phase, the speaker has to provide appropriate speech, such as continuous number strings, sentences, or phrases for building the speakers' models in the system. In the verification phase, the verified speech is substituted into the enrolled speaker models, and the similarity between the speech and the models is used to discriminate. We further design the whole system in a system-on-a-chip (SoC). We focus on the Mel-frequency cepstral coefficients (MFCCs) pre-processing module on FPGA and implement the lightweight the post-processing models such as Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) in software. A piece of speech data can be processed in 53.6ms to meet the real-time way. The proposed speaker verification system has a 93.3% accuracy rate. The overall architecture consumes only 4.26W on Xilinx ZCU104. Moreover, the proposed MFCC chip was implemented in TSMC 90nm, and the gate count is 276k at 1 volt while power consumption is 41.15 mW with a 200 MHz operating frequency. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: GMM-Based Speaker Verification System with Hardware MFCC in SoC Design.
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  Data: <searchLink fieldCode="AR" term="%22Tsai%2C+Tsung-Han%22">Tsai, Tsung-Han</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> han@ee.ncu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Chiao-Li%22">Wang, Chiao-Li</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Jun2024, Vol. 83 Issue 19, p56991-57010. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Taiwan+Semiconductor+Manufacturing+Co%2E+Ltd%2E%22">Taiwan Semiconductor Manufacturing Co. Ltd.</searchLink><br /><searchLink fieldCode="DE" term="%22Integrated+circuit+verification%22">Integrated circuit verification</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+mixture+models%22">Gaussian mixture models</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+on+a+chip%22">Systems on a chip</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink>
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  Data: In recent years, speaker verification has been extensively explored and has significantly improved its effectiveness. It analyzes the voiceprint characteristics of speakers and finds out the differences in voiceprint characteristics between speakers for verification. In this paper, we propose a text-dependent speaker verification system and its hardware implementation of the feature extraction. The proposed speaker verification system includes two phases: enrollment and verification. In the enrollment phase, the speaker has to provide appropriate speech, such as continuous number strings, sentences, or phrases for building the speakers' models in the system. In the verification phase, the verified speech is substituted into the enrolled speaker models, and the similarity between the speech and the models is used to discriminate. We further design the whole system in a system-on-a-chip (SoC). We focus on the Mel-frequency cepstral coefficients (MFCCs) pre-processing module on FPGA and implement the lightweight the post-processing models such as Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) in software. A piece of speech data can be processed in 53.6ms to meet the real-time way. The proposed speaker verification system has a 93.3% accuracy rate. The overall architecture consumes only 4.26W on Xilinx ZCU104. Moreover, the proposed MFCC chip was implemented in TSMC 90nm, and the gate count is 276k at 1 volt while power consumption is 41.15 mW with a 200 MHz operating frequency. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-023-17561-6
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        Text: English
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      – SubjectFull: Gaussian mixture models
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      – SubjectFull: Markov processes
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      – SubjectFull: Feature extraction
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              M: 06
              Text: Jun2024
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