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。
Emotion recognition of speech is signification for artificial intelligence research; the feature parameters distillate accuracy influences recognition-rate directly.
LPC声码器在准确提取声道参数的情况下，激励模型的优劣直接影响着综合语音的质量。
When LPC vocoder exactly extracts vocal tract parameters, whether an excitation model is good of not will severoly affect the quality of the synthetic speech.
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。
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.
语音识别在通信等领域有着广泛的用途，其中语音特征参数提取是语音识别系统的一个重要组成部分。
Speech recognition has wide use in the field of communication and so on. Speech feature parameter extraction is an important part of the speech recognition system.
本文采用语音信号的正弦表示方法并利用耳蜗模型提取了语音信号的基本特征参数，建立了一个语音分析／合成系统。
The sinusoidal representation of speech signal and a cochlear model are used to extract speech parameters in this paper, and a speech analysis/synthesis system is developed using the model.
主要研究用于分布式语音识别(dsr)的语音参数的提取方法以及参数性能分析。
This paper focuses on the extraction and performance analysis of speech parameters for Distributed speech Recognition (DSR).
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。
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.
分析了语音信号特征参数提取方法的优劣，并总结得出了参数提取的原则，并对模板训练匹配的问题进行了研究。
According analyse the way of feature parameter extraction, we get the principle of feature parameter extraction, also study the theory of the model-training and model - matching.
其中，声道参数的转换是获得高质量重建语音的关键，所以选择声道共振峰参数作为待转换的特征参数，利用线性预测求根法提取共振峰参数。
The vocal-tract mapping algorithm is the key part, so formant parameters which are estimated by the root-finding method based on LP analysis, are chosen for the transformation parameters.
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。
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).
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.
在说话人识别技术中，特征参数的提取对语音训练和识别有着非常重要的作用。
Speech feature parameter extraction is an very important part of the speech recognition system, especially in speech training and recognition.
在说话人识别技术中，特征参数的提取对语音训练和识别有着非常重要的作用。
Speech feature parameter extraction is an very important part of the speech recognition system, especially in speech training and recognition.
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