Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Averaging regularized estimators
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
A Consistency-Based Model Selection for One-Class Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A tutorial on text-independent speaker verification
EURASIP Journal on Applied Signal Processing
Pattern Recognition
Hi-index | 0.00 |
Speaker verification is a challenging problem in speaker recognition where the objective is to determine whether a segment of speech in fact comes from a specific individual. In supervised machine learning terms this is a challenging problem as, while examples belonging to the target class are easy to gather, the set of counter-examples is completely open. This makes it difficult to cast this as a supervised classification problem as it is difficult to construct a representative set of counter examples. So we cast this as a one-class classification problem and evaluate a variety of state-of-the-art one-class classification techniques on a benchmark speech recognition dataset. We construct this as a two-level classification process whereby, at the lower level, speech segments of 20 ms in length are classified and then a decision on an complete speech sample is made by aggregating these component classifications. We show that of the one-class classification techniques we evaluate, Gaussian Mixture Models shows the best performance on this task.