The vq parameter, which specifies a URL-encoded search term
vq参数指定一个URL 编码的搜索条件
Alas! First when I tried to use VQ I found that your cheeks belong to only you.
唉，当我第一次使用矢量量化，我发现你的面庞只属于你自己。
So, how to achieve global optimal codebook become the one of key problems of designing VQ algorithm.
因此，如何获得全局最优的码书成为矢量量化算法设计的主要研究问题之一。
VQ Model Based on Clustering Validity Analysis: it's the flaw for the codebook training algorithm that the size of codebook is choosed artificially.
基于聚类有效性分析的VQ模型：VQ模型电码本的训练算法有一个弱点：电码本大小是人为指定的。
This paper describes the three methods and gives experimental results and analysis in multi-speaker Chinese digit DHMM/VQ recognition in detail.
本文将详细介绍这三种方法，及其在多发育人汉语数字的DHMM/VQ语音识别中试验结果极及其分析。
The result of experiments shows that the complexity of VQ has been greatly reduced.
实验结果证明，该算法可以降低矢量量化的复杂度。
One of VQ image coding method used in the low frequency subband-the unbalanced tree-structured VQ is presented in this paper.
文章介绍一种图像低频子带编码的矢量量化方法—非对称树结构矢量量化。
VQ (Vector Quantization) technique is widely used in text-dependent and text-independent speaker recognition systems.
矢量量化(VQ)技术在话者识别系统中得到了广泛的应用。
The evolutionary algorithm is introduced into the design of COVQ to achieve a significant improvement of VQ performance for a given noisy channel status model.
该算法在给定信道状态模型和存在信道噪声的情况下，可以有效地提高矢量量化器的性能，实现了信道最优矢量量化器的设计。
Now we introduce trajectory model in speaker recognition and improve the codebook training algorithm for VQ.
文章把轨线模型应用于说话人识别，同时对VQ模型的电码本训练算法进行了改进。
VQ codebook design is essentially a classification of training vectors.
矢量量化码书设计本质是搜索训练矢量的最佳分类。
Vector Quantization (VQ) is one of the popular codebook design methods for text-independent speaker identification. The key problem of VQ is the design of codebook.
矢量量化(VQ)方法是文本无关说话人识别中广泛应用的建模方法之一，它的主要问题是码本设计问题。
This paper presents a new VQ method with different dimensions to vowels and consonants based on the quasi - periodicity of neighbour vowels which leads to strong correlations.
根据准周期性的韵母相邻周期具有很强相关性的特点，本文提出一种分别对声母和韵母采取不同维数的矢量量化的方法。
Firstly silence and non-silence are classified based on threshold judging and then VQ-GMM is used to further classify non-silence into speech, music and background.
首先通过阈值判决区分静音和非静音，然后利用VQ-GMM分类器将非静音进而分为语音、音乐和环境背景音。
Base on it, a new fast VQ search algorithm using Energy Band Segmentation is presented, which is recommended in the actual satellite codec system.
以此为研究背景，提出了一种新的基于能量分级的快速VQ搜索算法，并应用于实际卫星编解码系统中。
Image compression is one of the most important key techniques in image processing. Traditional compression methods include prediction coding, transform coding and vector quantization (VQ).
图像压缩是数字图像处理中最重要的关键技术之一，传统的图像压缩方法有预测编码、变换编码和矢量量化等。
Vector Quantization (VQ) is one of popular data compression and data coding methods for speech recognition at present.
矢量量化(VQ)是语音识别中广泛应用的一种数据压缩和编码方法。
Because of its features which include simple operation procedure and so on, speaker recognition based on VQ is widely applied to the field of speaker recognition.
基于矢量量化的说话人识别，因其运算过程简单等特点，在说话人识别领域有着广泛的应用。
Vector quantization (VQ) is an efficient data compression technique.
矢量量化(VQ)是一种有效的数据压缩技术。
This paper presents a new strategy of particle-pair(PP) for vector quantization(VQ) in image coding.
本文给出了一种新的图像矢量量化码书的优化设计方法——粒子对算法。
Vector quantization (VQ) is always more powerful than scalar quantization in theory.
在理论上上矢量量化的性能总是优于标量量化。
The larger the codebook size or the vector dimension is, the higher the computational complexity of the full-search VQ is.
码书大小和矢量维数越大，穷尽搜索矢量量化编码的计算复杂度就越高。
The experiment of using different phonetic parameter indicates that the VQ algorithm is an effective method in speaker recognition.
用不同语音参数进行实验，实验表明应用矢量量化的方法用在说话人识别中是一种有效方法。
An evolutionary algorithm based channel-optimized VQ (COVQ) design algorithm on noisy channel is presented in the paper.
本文提出了一个基于进化算法的信道最优矢量量化器(COVQ)设计算法。
Codebook design algorithms based on tabu search (TS) approach are presented for vector quantization (VQ).
本文提出了基于改进禁止搜索(TS)算法的矢量量化(VQ)码书设计方法。
This paper presents systematically fast search algorithms in VQ, and an efficient search algorithm is introduced.
本文系统地总结了矢量量化中的快速搜索算法，并在此基础上给出了一种有效的快速算法。
The algorithm achieves a significant improvement of COVQ performance for a given noisy channel status model over other conventional VQ design methods, as confirmed by experimental results.
采用该算法，在给定信道状态模型和信道噪声情况下，可有效地提高矢量量化器的性能，仿真实验结果表明该算法可获得比传统算法更优的性能增益。
Self-organizing neural network is a very efficient method for pattern recognition and vector quantization(VQ).
为有效提高矢量量化码书的性能和学习效率，需进一步改进自组织神经网络的学习算法。
Vector Quantization(VQ)is one of the popular codebook design methods for text-independent speaker identification.
矢量量化（VQ）方法是文本无关说话人识别中广泛应用的建模方法之一，它的主要问题是码本设计问题。
That is distilling one's character. Using VQ not only can avoid difficult speech subsection and time warping, but also it can reduce data store as a constringent method.
采用矢量量化可避免困难的语音分段问题和时间归整问题，且作为一种数据压缩手段可大大减少系统所需的数据存储量。
应用推荐