speaker verification

基本解释

The performance of speaker verification systems is often compromised under real-world environments.
说话人确认系统的性能往往是在真实世界的环境受到损害。
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The support vector machine based speaker verification models are trained on the enrolled speaker and the background model.
支持向量机用作说话人确认模型来训练目标说话人和背景说话人的语音数据。
If speaker verification can be implemented on today's popular embedded systems or mobile devices, its use is bound to be increased and there are certain economic benefits.
如果在嵌入式系统或者移动设备中加入说话人确认功能，其应用范围必然会增大，有一定的经济效益。

The Lenovo A586 smartphone is the first in the industry that incorporates this Speaker Verification technology into its operating system.
ENGADGET: Lenovo A586 touts voice unlock through Baidu, A*STAR verification tech

说话人确认
It can be classified into speaker identification and speaker verification according to decision modes. This thesis focuses attention on free-text speaker identification.
说话人识别可以分为说话人辨识和说话人确认两大类。
说话人认证
Currently, Gaussian Mixture Model-Universal Background Model based speaker verification, dominates the field of text-independent speaker verification.
目前,最热门的文本无关说话人认证系统均是基于高斯混合模型并结合背景模型的,这类系统忽略说话人说话的内容、语言等,因而其工程应用价值大打折扣。
话者识别

说话人确认
Aiming at improving the speed of speaker verification system based on kernel fisher discriminant,a new method based on PCA and kernel fisher discriminant is proposed.
针对核Fisher判别技术在说话人确认中实时性较差的问题,提出了一种基于PCA和核Fisher判别的说话人确认方法。
说话人认证
Text-dependent speaker verification system can be implemented through different mechanisms.
由于实现方法和使用方法的不同,文本相关的说话人认证可以有许多不同的实现方案,论文研究了用户定制密码的说话人认证和系统提示密码的说话人认证。
冒认者
Support Vector Machine(SVM)has been widely used in text-independent speaker verification systems. However,there are lots of training data unbalance problems with this algorithm due to the insufficiency of the data from target speakers.
支持向量机在与文本无关的话者确认系统中已经取得了广泛的应用,但是在实际应用系统中获得的目标说话人样本与冒认者样本数量比一般在几千分之一,因此存在很严重的样本非平衡问题,冒认者样本选择的好坏直接影响到整个系统的性能。
说话者验证