Adversarial representation learning for private speech generation

(creator_code:org_t)

Abstract Subject headings

As more data is collected in various settingsacross organizations, companies, and countries,there has been an increase in the demand of userprivacy. Developing privacy preserving methodsfor data analytics is thus an important area of research. In this work we present a model basedon generative adversarial networks (GANs) thatlearns to obfuscate specific sensitive attributes inspeech data. We train a model that learns to hidesensitive information in the data, while preservingthe meaning in the utterance. The model is trainedin two steps: first to filter sensitive informationin the spectrogram domain, and then to generatenew and private information independent of thefiltered one. The model is based on a U-Net CNNthat takes mel-spectrograms as input. A MelGANis used to invert the spectrograms back to rawaudio waveforms. We show that it is possible tohide sensitive information such as gender by generating new data, trained adversarially to maintainutility and realism.

By the author/editor: Ericsson, David; Östberg, Adam; Zec, Edvin Listo; Martinsson, John; Mogren, Olof

About the subject

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