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A study of speech recognition system based on the Hidden Markov Model with Gaussian-Mixture

机译:基于高斯混合隐马尔可夫模型的语音识别系统研究

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In this paper, we present a study of isolated word speech recognition system. The adopted system is based on the Hidden Markov Model with Gaussian Mixture (HMM-GM). We studied the recognition rate by varying the states number (3, 4, 5, 6 and 7 states) and the number of Gaussians per state (2, 4, 8, 12, 14 and 16 Gaussians) of Hidden Markov Model. We evaluated these recognition rates using two parameterization techniques Mel Frequency Cepstral Coefficients (MFCC) and Perceptual Linear Prediction (PLP). We have introduced the dynamic coefficients and the energy of the signal in order to achieve an improvement in the recognition rate.
机译:在本文中,我们提出了孤立词语音识别系统的研究。所采用的系统基于具有高斯混合的隐马尔可夫模型(HMM-GM)。我们通过改变隐马尔可夫模型的状态数(3、4、5、6和7个状态)和每个状态的高斯数(2、4、8、12、14和16个高斯数)来研究识别率。我们使用两种参数化技术梅尔频率倒谱系数(MFCC)和感知线性预测(PLP)评估了这些识别率。为了引入识别率的提高,我们引入了动态系数和信号能量。

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