| 大词汇连续汉语语音的MLP声学特征的研究 |
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tion results. However, for the poor of these short-term features on discrimination, neural network multilayer perceptron (MLP) are used to produce two types of discriminative features HATs and TANDEM instead of short features, and respectively, the corresponding GMM parameter models are trained. Experimental results show that the GMHMM the LVCSR system based on discriminative features is superior to the system traditional based on the short-term features; To further improve the system recognition rate, the two types of discriminative features HATs and TANDEM are combined as MLPs feature flow to retrain GMHMM, that leads to an absolute reduction of the character error rate (CER) of about 2%~3.8%.
Key words: MLP; discriminative features; HMM; GMM
对语音信号特征参数的研究是建立良好的语音识别系统的基础与关键。在过去的研究中,语音识别系统的特征提取成分主要包括频谱包络预测,特别是经过某些简单变化后的特征,目前前端大部分是基于短时轨迹(约10ms)信号分析的美尔倒谱(MFC)或是感知线性预测(PLP)。但这些传统的短时特征参数存在着对信号变化过于敏感,不能反映连续帧之间的相关特性,区分性差等方面的不足。近年来,国外很多语音研究机构在语音信号的特征提取、声学建模方面引入了神经网络ANN,其中由Berkele上一页 [1] [2] [3] [4] [5] [6] [7] 下一页 |
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