A small footprint i-vector extractor

Discriminatively Trained i-vector Extractor for Speaker Verification

We propose a strategy for discriminative training of the ivector extractor in speaker recognition. The original i-vector extractor training was based on the maximum-likelihood generative modeling, where the EM algorithm was used. In our approach, the i-vector extractor parameters are numerically optimized to minimize the discriminative cross-entropy error function. Two versions of the i-vector ...

VB calibration to improve the interface between phone recognizer and i-vector extractor

The EM training algorithm of the classical i-vector extractor [1, 2] is often incorrectly described as a maximum-likelihood method. The i-vector model is however intractable—the likelihood itself and the hidden-variable posteriors needed for the EM algorithm cannot be computed in closed form. We show here that the classical i-vector extractor recipe is actually a mean-field variational Bayes (V...

Assessment of Land Use Ecological Footprint in an Iranian Small City

RBM-PLDA subsystem for the NIST i-vector challenge

This paper presents the Speech Technology Center (STC) system submitted to NIST i-vector challenge. The system includes different subsystems based on TV-PLDA, TV-SVM, and RBM-PLDA. In this paper we focus on examining the third RBM-PLDA subsystem. Within this subsystem, we present our RBM extractor of the pseudo i-vector. Experiments performed on the test dataset of NIST-2014 demonstrate that al...

Evaluation of the Neurological State of People with Parkinson's Disease Using i-Vectors

The i-vector approach is used to model the speech of PD patients with the aim of assessing their condition. Features related to the articulation, phonation, and prosody dimensions of speech were used to train different i-vector extractors. Each i-vector extractor is trained using utterances from both PD patients and healthy controls. The i-vectors of the healthy control (HC) speakers are averag...