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A speaker recognition system using combination method between vector quantization and gaussian mixture model

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Speaker recognition is a biometric technique to recognize people’s identity based on their voice signal. A recognition system has two main requirements, which are high accuracy recognition rate and short processing time under large amount of training data. This paper propose a method to solve the two above requirements by performing a combination of two advantages of each VQ and GMM model to provide a new model can be called a “Hybrid VQ/GMM-UBM model”. This model not only takes the advantage of high accuracy in GMM method but also improve the accuracy rate and reduce the amount of computation of the system when combined with VQ model. | Nội dung Text A speaker recognition system using combination method between vector quantization and gaussian mixture model

TÀI LIỆU LIÊN QUAN

Master's thesis of Engineering: Channel compensation for speaker recognition systems

A speaker recognition system using combination method between vector quantization and gaussian mixture model

Overview of Speaker Recognition

Báo cáo khoa hoc:" Hypothesis testing for evaluating a multimodal pattern recognition framework applied to speaker detection"

Báo cáo hóa học: " Hypothesis testing for evaluating a multimodal pattern recognition framework applied to speaker detection"

Báo cáo hóa học: " Research Article Towards an Intelligent Acoustic Front End for Automatic Speech Recognition: Built-in Speaker "

Báo cáo hóa học: " Research Article Auditory Sparse Representation for Robust Speaker Recognition Based on Tensor Structure"

Báo cáo hóa học: " Research Article On the Use of Complementary Spectral Features for Speaker Recognition"

Báo cáo hóa học: " A Posterior Union Model with Applications to Robust Speech and Speaker Recognition"

Báo cáo hóa học: " Data-Model Relationship in Text-Independent Speaker Recognition"
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