AI SearchFeature extraction through spectral analysis for speech recognition
Call Number: AIT RSPR no. TC-95-14 Material type:
SeriesSeries: Asian Institute of Technology. Research studies project report ; no. TC-95-14Publication details: Bangkok : Asian Institute of Technology, 1995Description: 56 leavesSubject(s): Online resources: Dissertation note: Research Studies Project Report (M. Eng.) - Asian Institute of Technology, 1995 Summary: This study presents a method for extracting speech features through spectral analysis for speech recognition purposes. After a brief overview of all the major spectral analysis techniques available, LPC-Derived Bilinearly Transformed Cepstral Coefficients have been proposed as the working coefficients. A program has been developed in MATLAB environment for implementing the proposed algorithm and using real Thai speech (vowel parts of Thai syllables 'THI' and 'KHA:'), two codevectors are calculated. Through some calculations (e.g., mean or centroid, distance etc.), threshold values have been set for evaluating the performance of the generated codevectors.
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A research study submitted in partial fulfillment of .the requirements for the deg1·ee of Master of Engineering, School of Engineering and Technology
Research Studies Project Report (M. Eng.) - Asian Institute of Technology, 1995
This study presents a method for extracting speech features through spectral analysis for speech recognition purposes. After a brief overview of all the major spectral analysis techniques available, LPC-Derived Bilinearly Transformed Cepstral Coefficients have been proposed as the working coefficients. A program has been developed in MATLAB environment for implementing the proposed algorithm and using real Thai speech (vowel parts of Thai syllables 'THI' and 'KHA:'), two codevectors are calculated. Through some calculations (e.g., mean or centroid, distance etc.), threshold values have been set for evaluating the performance of the generated codevectors.
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