非线性加权能量特征在英语词重音检测中的应用

为了解决传统特征在重音检测中鲁棒性不高的问题,根据重音的定义,以单词为单位,考察词内各音素携带基音同步动态短时帧能量的差异,同时引入非线性加权因子,提出非线性加权能量特征。使用非线性加权能量特征以及与传统特征的特征组合对英语连续语音的实验结粜表明,非线性加权能量特征比传统特征鲁棒性更高,联合使用新特征与传统特征,可使系统误识率下降3.58%。

Application of nonlinear weighting energy feature in English lexical stress detection

A Nonlinear Weighting Energy feature,which is used to investigate the differences of pitch-synchronous dynamic frame-length energies among phonemes in each words is proposed,in order to solve the robustlessness problem of traditional features in lexical stress detection.The contribution of Nonlinear Weighting Energy feature to English lexical stress detection was evaluated with ISLE database.Experimental results show that the Nonlinear Weighting Energy feature is more robust than traditional features,while the combination of Nonlinear Weighting Energy and traditional features could provide a reduction of 3.58% in terms of error rates compared with the results using traditional features only.