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Time encoded signal processing and recognition with vector quantization: applied to Arabic numerals.

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Title: Time encoded signal processing and recognition with vector quantization: applied to Arabic numerals.
Authors: Lamkadam, Abdelmajid1 abdelmajid.lamkadam@usmba.ac.ma, Karim, Mohammed2 mohammed.karim@usmba.ac.ma
Source: Telkomnika. Apr2026, Vol. 24 Issue 2, p481-489. 9p.
Subjects: Vector quantization, Numerals, Automatic speech recognition, Signal processing, Acoustic signal processing, Fisher discriminant analysis
Abstract: This article presents our contribution to speaker recognition using Arabic numerals. This recognition is based on hybridization between the time encoded signal processing and recognition (TESPAR) technique and vector quantization (VQ), in order to consolidate the classification step thanks to this combination. To set up an effective and efficient recognition system, we used a corpus recorded under ideal conditions, minimizing the differences between the reference corpus and the test corpus. We also applied the linear discriminant analysis (LDA) technique in order to discriminate the acoustic vectors and minimize the representative space. This hybridization indicated a quantifiable increase in the speaker recognition rate with the ten Arabic numerals (0-9). [ABSTRACT FROM AUTHOR]
Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University 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.)
Database: Engineering Source
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  Data: Time encoded signal processing and recognition with vector quantization: applied to Arabic numerals.
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  Data: <searchLink fieldCode="JN" term="%22Telkomnika%22">Telkomnika</searchLink>. Apr2026, Vol. 24 Issue 2, p481-489. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Vector+quantization%22">Vector quantization</searchLink><br /><searchLink fieldCode="DE" term="%22Numerals%22">Numerals</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+speech+recognition%22">Automatic speech recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Acoustic+signal+processing%22">Acoustic signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Fisher+discriminant+analysis%22">Fisher discriminant analysis</searchLink>
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  Data: This article presents our contribution to speaker recognition using Arabic numerals. This recognition is based on hybridization between the time encoded signal processing and recognition (TESPAR) technique and vector quantization (VQ), in order to consolidate the classification step thanks to this combination. To set up an effective and efficient recognition system, we used a corpus recorded under ideal conditions, minimizing the differences between the reference corpus and the test corpus. We also applied the linear discriminant analysis (LDA) technique in order to discriminate the acoustic vectors and minimize the representative space. This hybridization indicated a quantifiable increase in the speaker recognition rate with the ten Arabic numerals (0-9). [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University 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: 10.12928/TELKOMNIKA.v24i2.27443
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      – Code: eng
        Text: English
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      – SubjectFull: Vector quantization
        Type: general
      – SubjectFull: Numerals
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      – SubjectFull: Automatic speech recognition
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      – SubjectFull: Signal processing
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      – SubjectFull: Acoustic signal processing
        Type: general
      – SubjectFull: Fisher discriminant analysis
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      – TitleFull: Time encoded signal processing and recognition with vector quantization: applied to Arabic numerals.
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              Text: Apr2026
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              Y: 2026
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