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Comparison of Khasi Speech Representations with Different Spectral Features and Hidden Markov States 下载免费PDF全文
Bronson Syiem Sushanta Kabir Dutta Juwesh Binong Lairenlakpam Joyprakash Singh 《电子科技学刊:英文版》2021,19(2):155-162
In this paper, we present a comparison of Khasi speech representations with four different spectral features and novel extension towards the development of Khasi speech corpora. These four features include linear predictive coding (LPC), linear prediction cepstrum coefficient (LPCC), perceptual linear prediction (PLP), and Mel frequency cepstral coefficient (MFCC). The 10-hour speech data were used for training and 3-hour data for testing. For each spectral feature, different hidden Markov model (HMM) based recognizers with variations in HMM states and different Gaussian mixture models (GMMs) were built. The performance was evaluated by using the word error rate (WER). The experimental results show that MFCC provides a better representation for Khasi speech compared with the other three spectral features. 相似文献
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