activity
20172021
most citedInvestigation of Using VAE for i-Vector Speaker Verification

6 citations · 6 across the 2 of their papers we have counts for

collaborators

5 papers

eess.AS2021

LT-LM: a novel non-autoregressive language model for single-shot lattice rescoring

Anton Mitrofanov, Mariya Korenevskaya, Ivan Podluzhny +7

Neural network-based language models are commonly used in rescoring approaches to improve the quality of modern automatic speech recognition (ASR) systems. Most of the existing met…

eess.AS2020

Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario

Ivan Medennikov, Maxim Korenevsky, Tatiana Prisyach +9

Speaker diarization for real-life scenarios is an extremely challenging problem. Widely used clustering-based diarization approaches perform rather poorly in such conditions, mainl…

cs.CL2018

Medical code prediction with multi-view convolution and description-regularized label-dependent attention

Najmeh Sadoughi, Greg P. Finley, James Fone +6

A ubiquitous task in processing electronic medical data is the assignment of standardized codes representing diagnoses and/or procedures to free-text documents such as medical repo…

cs.SD2018

Exploring End-to-End Techniques for Low-Resource Speech Recognition

Vladimir Bataev, Maxim Korenevsky, Ivan Medennikov +1

In this work we present simple grapheme-based system for low-resource speech recognition using Babel data for Turkish spontaneous speech (80 hours). We have investigated different…

cs.SD20176 cited

Investigation of Using VAE for i-Vector Speaker Verification

Timur Pekhovsky, Maxim Korenevsky

New system for i-vector speaker recognition based on variational autoencoder (VAE) is investigated. VAE is a promising approach for developing accurate deep nonlinear generative mo…