目前已知的语音特征包括频谱、自相关系数、能量、平均幅度、过零率、共振峰、线谱对、线性预测系数、线性预测倒谱(LPCC)、Mel频标倒谱(MFCC)等。线性预测倒谱参数(Linear Predic-tion Cepstrum Coefficient,LPCC)是基于语音
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1970年代,语音识别研究领域取得了突破性的进展,因为线性预测倒谱(Linear Predictive Cepstral Coding, LPCC)和动态时间规整技术(Dynamic Time Warping, DTW)的日趋成熟,研制成功了基于线...
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线性预测倒谱系数 LPCC ; Linear Prediction Cepstrum Coefficient ; Linear Prediction Cepstral Coding ; LinerPredictive CepstrumCoefficient
线性预测倒谱参数 LPCC ; Linear Prediction Cepstrum Coefficient
感知线性预测倒谱系数 PLPCC
线性预测美尔倒谱系数 LPMFCC
线性预测编码倒谱系数 Linear Prediction Cepstrum Coefficient ; LPCC ; linear predict code cepstral coefficients
本文应用全极点模型,提取语音信号的线性预测系数,并推导出其倒谱系数,获得线性预测倒谱差分,用以描述说话人声道的动态变化。
In this paper, we use full pole model to obtain speech signal LPC, then deduce it's LPCC, and we use the LPCC difference to describe speaker's track dynamic movement.
通过应用全极点模型,提取语音信号的线性预测系数,并推导出其倒谱系数,获得线性预测倒谱差分,用以描述说话人声道的动态变化。
By using full pole model, we obtained speech signal LPC, then deduced it's LPCC, and we used the LPCC difference to describe speaker's track dynamic movement.
将梅尔倒谱参数和线性预测参数结合起来作为语音识别的特征提取参数。
The MFCC coefficients and LPCC coefficients are combined as the speech recognition feature extraction parameters.
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