Analysis and extraction of LP-residual for its application in speaker verification system under uncontrolled noisy environment

详细信息    查看全文

• 作者：Songhita Misra ; Rabul Hussain Laskar ; U. Baruah…
• 关键词：LP ; residual ; Telephonic database ; Auto associative neural network ; FCM model ; K ; Means model ; LBG model ; Voice ; segment level approach ; Sentence level analysis ; Text dependent speaker verification system
• 刊名：Multimedia Tools and Applications
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：76
• 期：1
• 页码：757-784
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems;
• 出版者：Springer US
• ISSN：1573-7721
• 卷排序：76

文摘

Sub-segmental analysis of excitation source may contain significant speaker-specific information pertaining to speaker verification. In this paper, the excitation source feature has been explored for design of speaker verification system (SVS). The baseline of the system is extraction of speaker-specific information from LP-residual features by modelling the speakers through different supervised and unsupervised models, based on which they will be accepted or rejected. Direct LP-residual (DLR) as well as DCT coefficients of LP-residual (DCTLR) are approximated as the excitation source features for the system. The models are processed in two different level of analysis, namely, sentence level analysis as well as voice-segment level approach (VSLA), with the variations in the frame size of the input. Effects of the change of frame size in the input vectors are observed. Studies are carried over telephonic database collected in practical environment. A comparative analysis has been presented for the combination of models, features and the two levels of analysis for the given data. The experimental study suggests that application of VSLA on unsupervised models with DCTLR as input, provides a performance which is 14.21 % better than sentence level analysis of the models.