Long-term Conversation Analysis: Exploring Utility and Privacy
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Abstract (arXiv)
The analysis of conversations recorded in everyday life requires privacy protection. In this contribution, we explore a privacy-preserving feature extraction method based on input feature dimension reduction, spectral smoothing and the low-cost speaker anonymization technique based on McAdams coefficient. We assess the utility of the feature extraction methods with a voice activity detection and a speaker diarization system, while privacy protection is determined with a speech recognition and a speaker verification model. We show that the combination of McAdams coefficient and spectral smoothing maintains the utility while improving privacy.
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References · 11
- 2018VoxCeleb2: Deep Speaker Recognitioncited by 875
- 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 628
- 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 406
- 2020Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard taskscited by 73
- 2021ECAPA-TDNN Embeddings for Speaker Diarizationcited by 47
- 2021The VoicePrivacy 2020 Challenge: Results and findingscited by 46
- 2020Speaker anonymisation using the McAdams coefficientcited by 38
- 2019Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?cited by 22
- 2022The VoicePrivacy 2022 Challenge Evaluation Plancited by 18
- 2022Successes and critical failures of neural networks in capturing human-like speech recognitioncited by 7
- 2022MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generatorcited by 5
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