基本解釋[電子、通信與自動控制技術(shù)]說話人確認說話人認證話者識別[計算機科學技術(shù)]說話人確認說話人認證冒認者說話者驗證英漢例句雙語例句The performance of speaker verification systems is often compromised under real-world environments.說話人確認系統(tǒng)的性能往往是在真實世界的環(huán)境受到損害。zl50.comThe support vector machine based speaker verification models are trained on the enrolled speaker and the background model.支持向量機用作說話人確認模型來訓練目標說話人和背景說話人的語音數(shù)據(jù)。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.如果在嵌入式系統(tǒng)或者移動設備中加入說話人確認功能,其應用范圍必然會增大,有一定的經(jīng)濟效益。權(quán)威例句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 techspeaker verification更多例句詞組短語短語speaker verification system 說話人確認系統(tǒng)Speaker Verification with passwords 說話人確認與密碼speaker recognition identification verification 說話人識別Automatic Speaker Verification 自動說話人確認text -independent speaker verification 文本無關(guān)說話人確認speaker verification更多詞組專業(yè)釋義電子、通信與自動控制技術(shù)說話人確認It can be classified into speaker identification and speaker verification according to decision modes. This thesis focuses attention on free-text speaker identification.說話人識別可以分為說話人辨識和說話人確認兩大類。說話人認證Currently, Gaussian Mixture Model-Universal Background Model based speaker verification, dominates the field of text-independent speaker verification.目前,最熱門的文本無關(guān)說話人認證系統(tǒng)均是基于高斯混合模型并結(jié)合背景模型的,這類系統(tǒng)忽略說話人說話的內(nèi)容、語言等,因而其工程應用價值大打折扣。話者識別計算機科學技術(shù)說話人確認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.針對核Fisher判別技術(shù)在說話人確認中實時性較差的問題,提出了一種基于PCA和核Fisher判別的說話人確認方法。說話人認證Text-dependent speaker verification system can be implemented through different mechanisms.由于實現(xiàn)方法和使用方法的不同,文本相關(guān)的說話人認證可以有許多不同的實現(xiàn)方案,論文研究了用戶定制密碼的說話人認證和系統(tǒng)提示密碼的說話人認證。冒認者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.支持向量機在與文本無關(guān)的話者確認系統(tǒng)中已經(jīng)取得了廣泛的應用,但是在實際應用系統(tǒng)中獲得的目標說話人樣本與冒認者樣本數(shù)量比一般在幾千分之一,因此存在很嚴重的樣本非平衡問題,冒認者樣本選擇的好壞直接影響到整個系統(tǒng)的性能。說話者驗證