Search Results for author:
Found 18 papers, 5 papers with code
Keynote Speech - Voice anonymization and the GDPR
Talk for the Workshop on Legal and Ethical Issues in Human Language Technologies, LREC 2022, Marseille, 24 June 2022
Learnings from Technological Interventions in a Low Resource Language: Enhancing Information Access in Gondi
The primary obstacle to developing technologies for low-resource languages is the lack of representative, usable data.
The VoicePrivacy 2020 Challenge Evaluation Plan
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 evaluation methodology, and benchmarking solutions through a series of challenges.
Differentially Private Speaker Anonymization
We remove speaker information from these attributes by introducing differentially private feature extractors based on an autoencoder and an automatic speech recognizer, respectively, trained using noise layers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
The VoicePrivacy 2020 Challenge: Results and findings
We provide a systematic overview of the challenge design with an analysis of submitted systems and evaluation results.
Design Choices for X-vector Based Speaker Anonymization
The recently proposed x-vector based anonymization scheme converts any input voice into that of a random pseudo-speaker.
Introducing the VoicePrivacy Initiative
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 evaluation methodology, and benchmarking solutions through a series of challenges.
Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi
The primary obstacle to developing technologies for low-resource languages is the lack of usable data.
Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?
In this paper, we focus on the protection of speaker identity and study the extent to which users can be recognized based on the encoded representation of their speech as obtained by a deep encoder-decoder architecture trained for ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Evaluating Voice Conversion-based Privacy Protection against Informed Attackers
In this paper, we investigate anonymization methods based on voice conversion.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
End-to-End ASR for Code-switched Hindi-English Speech
While the lack of data adversely affects the performance of end-to-end models, we see promising improvements with MTL and balancing the corpus.
Phone Merging For Code-Switched Speech Recognition
Though the best performance gain of 1. 2{\%} WER was observed with manually merged phones, we show experimentally that the manual phone merge is not optimal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Spoken English Intelligibility Remediation with PocketSphinx Alignment and Feature Extraction Improves Substantially over the State of the Art
We use automatic speech recognition to assess spoken English learner pronunciation based on the authentic intelligibility of the learners' spoken responses determined from support vector machine (SVM) classifier or deep learning neural network model predictions of transcription correctness.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A language model based approach towards large scale and lightweight language identification systems
Most of the existing LID systems rely on modeling the language discriminative information from low-level acoustic features.
