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 chosen speech feature parameters have great effects on robustness and real-time of the speech recognition system.
特征参数的选取对整个语音识别系统的实时性、鲁棒性有很大的影响。
This text introduced the concept of speaker recognition system firstly, then analyzed a few extraction methods of speech feature parameters in common use and a few models of speaker recognition.
本文首先介绍了说话人识别系统的概念，然后分析了几种常用的语音特征参数的提取方法以及说话人识别的几种模型。
Emotion recognition of speech is signification for artificial intelligence research; the feature parameters distillate accuracy influences recognition-rate directly.
语音情感识别是人工智能的重要研究领域之一，特征参数提取的准确性直接影响识别的效果。
In this paper we introduced the speech signal feature parameters, the speech signal feature parameters selection, and introduced short-term energy, short-term average range extraction.
本文对语音信号特征参数、语音信号特征参数的选择进行了介绍，并介绍了语音信号的短时能量、短时平均幅度的提取。
The design principle of SOM and the effects of different feature parameters to speech recognition results are analyzed and discussed.
分析讨论了语音识别研究中，自组织神经网络的设计原则以及不同的特征参数等方面对语音识别结果的影响。
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。
To improve the performance of speaker recognition in the condition of noise and little speech data, feature parameters were studied based on the Vector Quantization (VQ).
为了使说话人识别系统在语音较短和存在噪声的环境下也具有较高的识别率，基于矢量量化识别算法，对提取的特征参数进行研究。
In order even better to extract feature parameters of speakers, speech should be filtered as a pre-processing process.
为了更好地提取说话人的特征，对语音进行滤波的预处理。
In order even better to extract feature parameters of speakers, speech should be filtered as a pre-processing process.
为了更好地提取说话人的特征，对语音进行滤波的预处理。
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