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논문 기본 정보
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- 학술저널
- 저자정보
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- 장전수학회 Proceedings of the Jangjeon Mathematical Society Proceedings of the Jangjeon Mathematical Society(장전수학회 논문집) 제24권 제4호
- 발행연도
- 2021.10
- 수록면
- 429 - 438 (10page)
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Features play a vital role in Automatic Speech Recognition (ASR). They also a ect the performance of speech recognizers in all environments to an extent. There are many types of features used in automatic speech recognition. Each feature type has its own signi cance depending upon its unique characteristics. LPC (Linear Predictive Coding), LPCC (Linear Predictive Cepstral Coe cients), Mel Frequency Cepstral Coe cients (MFCC), Perceptual Linear Prediction (PLP) and Relative spectral Analysis (RASTA) are some of the features that are generally used. Better results can be obtained by combining coe cients of di erent features. For instance, MFCC and PLP show better results when they are jointly used to recognize a speech signal. This paper aims to compare the performance of di erent features using ANN for same number of speech datasets. The result shows that proposed technique increases the recognition rate by 8.33% as compared to MFCC alone.
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참고문헌 (16)
참고문헌 신청
M. Anusuya / 2011 / Comparison of different speech feature extraction techniques with and without wavelet transform to kannada speech recognition / International Journal of Computer Applications 26
Namrata Dave / 2013 / Feature extraction methods lpc, plp and mfcc in speech recognition / International Journal For Advance Research in Engineering And Technology 1
S. Davis / 1980 / Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences / IEEE Transactions on Acoustics, Speech, and Signal Processing 28(4):357~366
C. O. Dumitru / 2006 / A comparative study of feature extraction methods applied to continuous speech recognition in romanian language / Proceedings ELMAR 2006 :115~118
Hynek Hermansky / 1990 / Perceptual linear predictive (PLP) analysis of speech / Journal of the Acoustical Society of America 87(4):1738~1752
Namrata Dave / 2013 / Feature extraction methods lpc, plp and mfcc in speech recognition / International Journal For Advance Research in Engineering And Technology 1
S. Davis / 1980 / Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences / IEEE Transactions on Acoustics, Speech, and Signal Processing 28(4):357~366
C. O. Dumitru / 2006 / A comparative study of feature extraction methods applied to continuous speech recognition in romanian language / Proceedings ELMAR 2006 :115~118
Hynek Hermansky / 1990 / Perceptual linear predictive (PLP) analysis of speech / Journal of the Acoustical Society of America 87(4):1738~1752
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