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20222025
most citedNeural Target Speech Extraction: An Overview

120 citations · 126 across the 17 of their papers we have counts for

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7 papers · 1 filter

eess.AS2024

Pretraining End-to-End Keyword Search with Automatically Discovered Acoustic Units

Bolaji Yusuf, Jan "Honza" Černocký, Murat Saraçlar

End-to-end (E2E) keyword search (KWS) has emerged as an alternative and complimentary approach to conventional keyword search which depends on the output of automatic speech recogn…

eess.AS2024

Probing Self-supervised Learning Models with Target Speech Extraction

Junyi Peng, Marc Delcroix, Tsubasa Ochiai +4

Large-scale pre-trained self-supervised learning (SSL) models have shown remarkable advancements in speech-related tasks. However, the utilization of these models in complex multi-…

eess.AS2024

Target Speech Extraction with Pre-trained Self-supervised Learning Models

Junyi Peng, Marc Delcroix, Tsubasa Ochiai +3

Pre-trained self-supervised learning (SSL) models have achieved remarkable success in various speech tasks. However, their potential in target speech extraction (TSE) has not been…

eess.AS2023

DiaCorrect: Error Correction Back-end For Speaker Diarization

Jiangyu Han, Federico Landini, Johan Rohdin +5

In this work, we propose an error correction framework, named DiaCorrect, to refine the output of a diarization system in a simple yet effective way. This method is inspired by err…

eess.AS20235 cited

End-to-End Open Vocabulary Keyword Search With Multilingual Neural Representations

Bolaji Yusuf, Jan Cernocky, Murat Saraclar

Conventional keyword search systems operate on automatic speech recognition (ASR) outputs, which causes them to have a complex indexing and search pipeline. This has led to interes…

eess.AS2023

Improving Speaker Verification with Self-Pretrained Transformer Models

Junyi Peng, Oldřich Plchot, Themos Stafylakis +3

Recently, fine-tuning large pre-trained Transformer models using downstream datasets has received a rising interest. Despite their success, it is still challenging to disentangle t…