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

Brij Mohan Lal Srivastava 1 Aurélien Bellet 1 Marc Tommasi 1 Emmanuel Vincent 2
1 MAGNET - Machine Learning in Information Networks
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
2 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : 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 decoding. As the speech signal carries a lot of information about the speaker, this raises serious privacy concerns. As a solution, an encoder may reside on each user device which performs local computations to anonymize the representation. 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. Through speaker identification and verification experiments on the Librispeech corpus with open and closed sets of speakers, we show that the representations obtained from a standard architecture still carry a lot of information about speaker identity. We then propose to use adversarial training to learn representations that perform well in ASR while hiding speaker identity. Our results demonstrate that adversarial training dramatically reduces the closed-set classification accuracy, but this does not translate into increased open-set verification error hence into increased protection of the speaker identity in practice. We suggest several possible reasons behind this negative result.
Complete list of metadatas

Cited literature [38 references]  Display  Hide  Download

https://hal.inria.fr/hal-02166434
Contributor : Aurélien Bellet <>
Submitted on : Wednesday, July 3, 2019 - 9:59:13 AM
Last modification on : Tuesday, September 10, 2019 - 11:32:02 AM

File

srivastava_IS19.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02166434, version 1

Citation

Brij Mohan Lal Srivastava, Aurélien Bellet, Marc Tommasi, Emmanuel Vincent. Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?. INTERSPEECH 2019 - 20th Annual Conference of the International Speech Communication Association, Sep 2019, Graz, Austria. ⟨hal-02166434⟩

Share

Metrics

Record views

372

Files downloads

571