The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Critical Band Subspace-Based Speech Enhancement Using SNR and Auditory Masking Aware Technique
IEICE - Transactions on Information and Systems
Analysis of Feature Extraction and Channel Compensation in a GMM Speaker Recognition System
IEEE Transactions on Audio, Speech, and Language Processing
Speaker Verification Using Support Vector Machines and High-Level Features
IEEE Transactions on Audio, Speech, and Language Processing
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Dynamic fixed-point arithmetic design of embedded SVM-Based speaker identification system
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
A new hybrid and dynamic fusion of multiple experts for intelligent porch system
Expert Systems with Applications: An International Journal
VLSI design of an SVM learning core on sequential minimal optimization algorithm
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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This paper presents a ubiquitous and robust text-independent speaker recognitionarchitecture for home automation digital life. In this architecture, a multiple microphone configuration is adopted to receive the pervasive speech signals. The multi-channel speech signals are then added together with a mixer. In a ubiquitous computing environment, the received speech signal is usually heavily corrupted by background noises. An SNR-aware subspace speech enhancement approach is used as a pre-processing to enhance the mixed signal. Considering the text-independent speaker recognition, this paper applies a multi-class support vectors machine (SVM)[10][11] instead of conventional Gaussian mixture models (GMMs)[12]. In our experiments, the speaker recognition rate can averagely reach 97.2% with the proposed ubiquitous speaker recognitionarchitecture.