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20152022
most citedThe VoicePrivacy 2020 Challenge: Results and findings

106 citations · 371 across the 12 of their papers we have counts for

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Showing cs.CLShow all

7 papers · 1 filter

cs.CL2022

Learnings from Technological Interventions in a Low Resource Language: Enhancing Information Access in Gondi

Devansh Mehta, Harshita Diddee, Ananya Saxena +8

The primary obstacle to developing technologies for low-resource languages is the lack of representative, usable data. In this paper, we report the deployment of technology-driven…

cs.CL2022★ 26 cited

The VoicePrivacy 2020 Challenge Evaluation Plan

Natalia Tomashenko, Brij Mohan Lal Srivastava, Xin Wang +8

The VoicePrivacy Challenge aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the…

cs.CL2021★ 106 cited

The VoicePrivacy 2020 Challenge: Results and findings

Natalia Tomashenko, Xin Wang, Emmanuel Vincent +11

This paper presents the results and analyses stemming from the first VoicePrivacy 2020 Challenge which focuses on developing anonymization solutions for speech technology. We provi…

cs.CL2020★ 100 cited

Introducing the VoicePrivacy Initiative

Natalia Tomashenko, Brij Mohan Lal Srivastava, Xin Wang +8

The VoicePrivacy initiative aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the…

cs.CL2020★ 3 cited

Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi

Devansh Mehta, Sebastin Santy, Ramaravind Kommiya Mothilal +7

The primary obstacle to developing technologies for low-resource languages is the lack of usable data. In this paper, we report the adoption and deployment of 4 technology-driven m…

cs.CL2019★ 42 cited

Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?

Brij Mohan Lal Srivastava, Aurélien Bellet, Marc Tommasi +1

Automatic speech recognition (ASR) is a key technology in many services and applications. This typically requires user devices to send their speech data to the cloud for ASR decodi…

cs.CL2019★ 79 cited

Evaluating Voice Conversion-based Privacy Protection against Informed Attackers

Brij Mohan Lal Srivastava, Nathalie Vauquier, Md Sahidullah +3

Speech data conveys sensitive speaker attributes like identity or accent. With a small amount of found data, such attributes can be inferred and exploited for malicious purposes: v…