Adversarial Representation Learning for Synthetic Replacement of Sensitive Speech Data

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dc.contributor.authorÖstberg, Adam
dc.contributor.authorEricsson, David
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.examinerMostad, Petter
dc.contributor.supervisorListo Zec, Edvin
dc.date.accessioned2020-09-08T08:39:09Z
dc.date.available2020-09-08T08:39:09Z
dc.date.issued2020sv
dc.date.submitted2020
dc.description.abstractAs more data is collected in various settings across organizations, companies, and countries, there has been an increase in the demand of user privacy. Developing privacy preserving methods for data analytics is thus an important area of research. In this work we present a model based on generative adversarial networks (GANs) that learns to obfuscate specific sensitive attributes in speech data. We train a model that learns to hide sensitive information in the data, while preserving the meaning in the utterance. The model is trained in two steps: first to filter sensitive information in the spectrogram domain, and then to generate new and private information independent of the filtered one. The model is based on a CNN that takes mel-spectrograms as input. A MelGAN is used to invert the spectrograms back to raw audio waveforms. We show that it is possible to hide sensitive information such as gender by generating new data, trained adversarially to maintain utility and realism.sv
dc.identifier.coursecodeMVEX03sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/301651
dc.language.isoengsv
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectgenerative adversarial networks, adversarial representation learning, deep learning, privacy, speech generationsv
dc.titleAdversarial Representation Learning for Synthetic Replacement of Sensitive Speech Datasv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
local.programmeEngineering mathematics and computational science (MPENM), MSc
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