[PDF] Top 20 A multi spectral data fusion approach to speaker recognition
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A multi spectral data fusion approach to speaker recognition
... In a seminal and influential paper, Allen [4] pop- ularized the earlier notion of Harvey Fletcher that the decoding of speech signals by humans is based on decisions in narrow frequency bands that are processed ... See full document
8
Acoustic Feature Extraction and Optimized Neural Network based Classification for Speaker Recognition
... For recognition process, probabilistic LDA method is used in several kinds of ...the speaker verification task with DNN and contribute there result to the classification process ...with speaker ... See full document
10
British Sign Language Recognition via Late Fusion of Computer Vision and Leap Motion with Transfer Learning to American Sign Language
... late fusion approach to multi-modality in sign language recognition improves the overall ability of the model in comparison to the singular approaches of Computer Vision ...Motion data ... See full document
14
Performance Improvement Of Data Fusion Based Real Time Multi Layered Gesture Recognition Using One-Shot Learning Approach With Kinect V2.
... Gesture recognition offers a new medium for human-computer interaction that can be both efficient and highly ...gesture recognition system should be able to work with a variety of input ...gesture ... See full document
7
Biomimetic multi-resolution analysis for robust speaker recognition
... our approach no model components have been customized in any way to deal with a specific noise condition, making it suitable for a wide range of acoustic ...the speaker recognition task and can in ... See full document
10
Speaker Recognition by Combining Gaussian Mixture Model (GMM) Spectral Representation and Phase Information
... extraction, speaker modeling and speaker testing ...extracting speaker-specific features from the speech signal at reduced data ...generate speaker models. The speaker models are ... See full document
9
Speaker Recognition: A Survey
... a speaker identification system proposed are : the feature extraction and the classification ...both fusion methods lead to enhanced performance. Speaker recognition is the concept of using a ... See full document
9
Data Model Relationship in Text Independent Speaker Recognition
... training data are known to be influential factors in speaker recognition per- formance and these factors are application ...of data might be relatively low due to background noise and possible ... See full document
11
Multi Spectral Inter-Correlative Approach for Feature Selection in Pattern Recognition
... of data information in a database, the memory consumption is observed to be very ...image Data in a feature format revealing the ...the data in more features format improves retrieval accuracy, more ... See full document
6
Applying Score Reliability Fusion to Bi Model Emotional Speaker Recognition
... opment data by using our two Fusion Weight Estimating ...bi-model approach achieve good results in speaker recognition assessment experiment on MASC text ...bi-model approach in ... See full document
6
Multi-speaker frequency warping vocal tract length normalization for speaker independent speech recognition
... in speaker independent speech recognition system is to compensate speaker ...variability. Speaker variability is usually related to the physical difference in vocal tract ...specific ... See full document
30
On the Use of Complementary Spectral Features for Speaker Recognition
... Each GMM was trained with 20 seconds of silence- removed clean speech. The remaining speech was segmented into 7 s utterances and used to test the speaker models un- der noisy and noise-free conditions. A total of ... See full document
10
Speaker Recognition and Gender Identification using Artificial Neural Network and Support Vector Machine
... speech. Recognition relies on English words as a recognition ...For speaker recognition speech features used are Mel Frequency Cepstral Coefficients (MFCCs), Energy, Pitch and Zero Crossing ... See full document
6
Hyper Spectral Image Classification using Multi Labelled, Multi Scale and Multi Angle CNN with MS MA BT Algorithm
... pictures. Spectral-spatial multi-functions can be used to efficiently enhance ranking accuracy ...a multi-scale MS-MA BT CNN algorithm for semantic segmentation of the SAR ... See full document
6
Text dependent Speaker Recognition by Combination of LBG VQ and DTW for Persian Language
... speech data. During the test, the TI speaker recognition system tries to find which speaker model (distribution) the test feature-vector-set came ...(TD) speaker recognition ... See full document
5
A Purely End to End System for Multi speaker Speech Recognition
... each speaker. Without this complementarity constraint, our direct multi- speaker recognition system could be susceptible to redundant recognition of the same ... See full document
11
Partitional Clustering Techniques for Multi-Spectral Image Segmentation
... We also make a comparative study on two partitional clustering techniques both based on the pdf estimation. The results show that estimating the entire pdf may reveal more details about the data than estimating ... See full document
8
Systematic Review On Speech Recognition Tools And Techniques Needed For Speech Application Development
... Speech Recognition also called Speech to Text Synthesis and Text to Speech Synthesis ...speech recognition system is to allow computer systems to accurately recognize human speech and the best way to ... See full document
11
IJCSMC, Vol. 3, Issue. 1, January 2014, pg.441 – 446 SURVEY ARTICLE A Survey Paper on Image Segmentation with Thresholding
... The work in this paper is motivated from a practical point of view by several disadvantages of existing methods. The first problem is the incapability of all known methods to appropriately segment objects from the ... See full document
6
Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Mobile Devices’ Sensors
... acquisition; data processing; data fusion; pattern recognition; machine learning.. 28 29.[r] ... See full document
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