Mapping the filtered MFCC to loudness the process of human perception is simulated.
将滤波后的参数映射为响度，由此模拟人的感知过程。
The experiment results show that the system built on DWT-MFC has better noise robustness than on LPCC and MFCC.
实验表明，与（LPCC）和（MFCC）参数相比，采用DWT-MFC参数使系统的噪声鲁棒性得到显著的提高。
During the experiment, MFCC (Mel Frequency Ceptral Coefficient) is adopted to speaker speech feature parameters.
实验中，采用美尔倒谱系数(MFCC)作为话者语音特征参数。
The MFCC coefficients and LPCC coefficients are combined as the speech recognition feature extraction parameters.
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 experiment results indicate that the new feature parameter WPP is able to outperform SBC and SBC is better than MFCC.
实验证明新特征参数WPP的语音识别性能优于SBC，而SBC的识别性能优于MFCC。
MFCC USES intermediate clustering results in one type of feature space to help the selection in other types of feature Spaces.
MFCC充分利用了一个特征空间的中间聚类结果来帮助另一个特征空间进行特征选择。
The influences of rapidly changing signals to human perception can be stressed by filtering the relative spectra (RASTA) for MFCC.
对MFCC进行相对谱(RASTA)滤波，可以突出快变信号对听觉感知的影响。
This feature vector made the Gaussian Mixture Model (GMM) classifier outperform MFCC and Differential MFCC features in classification.
该混合特征使得高斯混合模型(GMM)分类器可获得比使用MFCC特征及其差分MFCC更好的分类性能。
This paper proposes a high frequency weighted MFCC extraction method to improve the performance of speaker verification in noise conditions.
本文提出了一种可提高噪声环境下的说话人确认识别率的语音MFCC参数高频加权方法。
MFCC can express speech spectrum more accurately at low frequency, so MFCC is a good method to express the spectral envelope in voice conversion;
MFCC可以更加准确地表示语音低频处的频谱包络，在语音转换中是一种很好的频谱包络表示方法；
MFCC parameters is main describes the spectrum envelope features, which is used to state the vacal track characterizatics, while ignoring the impact of pitch frequency.
MFCC参数主要描述了表征声道特性的谱包络特征，而忽略了基音频率对它的影响。
The results of simulation and FPGA test show this kind of design is correct, which meets the requirement of real-time and precision in MFCC computation for speaker recognition.
通过仿真与FPGA测试，验证了该设计的正确性，能够满足说话人识别中MFCC参数提取的实时性要求和精度要求。
Also, since MFCC represent hearing frequency nonlinear characteristic, we utilize MFCC to be another speak recognition characteristic parameter to distinguish the input passwords.
利用听觉频率非线性特性的美尔倒谱作为语音识别的特征参数，来辨识说话人提供的输入口令。
The new algorithm is compared with traditional PID algorithm through simulation, and its practical application - the temperature control of small electrical furnace for MFCC unit is introduced.
对其进行了仿真，与常规pid算法进行了比较，并将其应用于小型电加热炉的控制中。
Selected for use MFCC and the difference and divided the characteristic parameter as phonetic recognition, to describe the non-linear characteristic of frequency of sense of hearing of ears of people.
选用美尔倒谱系数及其差分作为语音识别的特征参数，来描述人耳的听觉频率非线性特性。
Selected for use MFCC and the difference and divided the characteristic parameter as phonetic recognition, to describe the non-linear characteristic of frequency of sense of hearing of ears of people.
选用美尔倒谱系数及其差分作为语音识别的特征参数，来描述人耳的听觉频率非线性特性。
应用推荐