Two classifiers score fusion for text independentspeaker verification

The one-class classifiers compare, for each document of the known authorship, the dissimilarity between this document and the most dissim- ilar other document of this authorship to

The approach this year used probabilistic text retrieval based on logistic regression and incorporating blind rele- vance feedback for all of the runs and in addition we ran a number

Quatieri, Speaker verification using text-constrained Gaussian mixture models, in: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. Shriberg,

In order to model the TSI of speech while achieving high modeling performance, low cost decoding and enabling an efficient spotting mode, we propose a new target event modeling

To address the research objectives described in Section 1, we chose to train traditional machine learning algorithms only [3, 12], but combine them with each other and with

Automatic extraction of identity cues from personal traits (e.g., fingerprints, speech, and face images) has given rise to a particular branch of pattern

The proposed Hybrid Wavelet Scattering Transform Convolutional Neural Network (HWSTCNN) architecture..

5O , a fusion module P which merges the outputs from subset of individual classifiers, a decision module Q which assigns the test pattern to one of the classes and a selection