1) perceptual linear predictive cepstrum coefficient
感知线性预测倒谱系数
1.
The perceptual linear predictive cepstrum coefficient is extracted from the reconstructed spectrum and is combined with .
算法基于重建浊音频谱提取感知线性预测倒谱系数,与基音相组合作为说话人的语音特征参数矢量,采用高斯混合模型对说话人进行建模。
2) LPCC
线性预测倒谱系数
1.
This paper introduces (Linear Prediction Cepstrum Coefficient,LPCC)extraction algorithm, proposes a floating-point IP core implementation model of this algorithm, and describes the design of each sub-module in detail.
介绍了线性预测倒谱系数(Linear Prediction Cepstrum Coefficient,LPCC)提取算法,给出该算法的一种浮点IP核实现模型,并详细描述了各个子模块的设计方法。
2.
Taking LPCC as the voice feature parameters,the VQ as the model matching method,the correlative experiment is carried on.
采用线性预测倒谱系数(linear prediction cepstrum coefficient,LPCC)作为语音的特征参数,矢量量化(vector quantity,VQ)方法进行模式匹配,探讨声纹识别以实现身份认证,并对此识别方法进行了相关的实验。
3) linear predict code cepstral coefficients
线性预测编码倒谱系数
4) LP Mel Cepstrum Coeficient(LPMCC)
线性预测美尔倒谱系数
5) LPCC
线形预测倒谱系数
1.
This article unified the broad Mel frequency cepstrum coefficient(MFCC) and linear forecast cepstrum coefficient(LPCC) as the characteristic parameter of the rolling bearing audio signals,and used neural network method that had the strong learning capability to carry on the fault diagnosis.
本文结合语音识别中运用较广的美尔频率倒谱系数(MFCC)和线形预测倒谱系数(LPCC)作为滚动轴承音频信号的特征参数,并使用具有强学习能力的神经网络方法进行故障诊断,最后依靠Dempster-Shafrg(D-S)证据理论进行分析得出可信度高的判定结果。
6) linear prediction cepstrum coefficient
线性预测倒谱参数
1.
It regards linear prediction cepstrum coefficient(LPCC) as the characteristic parameter,and adopts the dynamic time warping(DTW) model.
系统以线性预测倒谱参数为特征参数,采用动态时间归整识别模型,在小词汇量特定人条件下,该系统的正识率可达到98%以上,在非特定人情况下正识率在93%以上。
补充资料：数理统计预测法(见发生量预测)
数理统计预测法(见发生量预测)
数理统计预测法见发生皿预测。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条