A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
On the Approximability of the Maximum Common Subgraph Problem
STACS '92 Proceedings of the 9th Annual Symposium on Theoretical Aspects of Computer Science
Fast Agglomerative Clustering Using a k-Nearest Neighbor Graph
IEEE Transactions on Pattern Analysis and Machine Intelligence
Explicit modelling of session variability for speaker verification
Computer Speech and Language
Speaker and Session Variability in GMM-Based Speaker Verification
IEEE Transactions on Audio, Speech, and Language Processing
An overview of text-independent speaker recognition: From features to supervectors
Speech Communication
Biometric identification based on the eye movements and graph matching techniques
Pattern Recognition Letters
Is masking a relevant aspect lacking in MFCC? A speaker verification perspective
Pattern Recognition Letters
Near-duplicate document image matching: A graphical perspective
Pattern Recognition
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Technical mismatches between the training and matching conditions adversely affect the performance of a speaker recognition system. In this paper, we present a matching scheme which is invariant to feature rotation, translation and uniform scaling. The proposed approach uses a neighborhood graph to represent the global shape of the feature distribution. The reference and test graphs are aligned by graph matching and the match score is computed using conventional template matching. Experiments on the NIST-1999 SRE corpus indicate that the method is comparable to conventional Gaussian mixture model (GMM) and vector quantization (VQ)-based approaches.