activity
20202025
most citedTarget-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario

198 citations · 307 across the 14 of their papers we have counts for

collaborators

14 papers

eess.AS2025

NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding

Vladimir Bataev, Andrei Andrusenko, Lilit Grigoryan +3

Statistical n-gram language models are widely used for context-biasing tasks in Automatic Speech Recognition (ASR). However, existing implementations lack computational efficiency…

eess.AS2024★ 1 cited

Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter

Andrei Andrusenko, Aleksandr Laptev, Vladimir Bataev +2

Accurate recognition of rare and new words remains a pressing problem for contextualized Automatic Speech Recognition (ASR) systems. Most context-biasing methods involve modificati…

eess.AS2023

The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System

Tae Jin Park, He Huang, Ante Jukic +7

We present the NVIDIA NeMo team's multi-channel speech recognition system for the 7th CHiME Challenge Distant Automatic Speech Recognition (DASR) Task, focusing on the development…

eess.AS2023★ 5 cited

Confidence-based Ensembles of End-to-End Speech Recognition Models

Igor Gitman, Vitaly Lavrukhin, Aleksandr Laptev +1

The number of end-to-end speech recognition models grows every year. These models are often adapted to new domains or languages resulting in a proliferation of expert systems that…

eess.AS2023★ 3 cited

Powerful and Extensible WFST Framework for RNN-Transducer Losses

Aleksandr Laptev, Vladimir Bataev, Igor Gitman +1

This paper presents a framework based on Weighted Finite-State Transducers (WFST) to simplify the development of modifications for RNN-Transducer (RNN-T) loss. Existing implementat…

eess.AS2022★ 10 cited

Fast Entropy-Based Methods of Word-Level Confidence Estimation for End-To-End Automatic Speech Recognition

Aleksandr Laptev, Boris Ginsburg

This paper presents a class of new fast non-trainable entropy-based confidence estimation methods for automatic speech recognition. We show how per-frame entropy values can be norm…