Abstract

Speech Emotion Recognition (SER) techniques have gained considerable interest in many applications including smart virtual assistants and health state tracking. SER systems often acquire and transmit speech data collected at the client-side to remote cloud platforms for inference and decision making. However, speech data carries rich information not only about emotions conveyed in vocal expressions, but also other sensitive demographic traits, such as gender, age, and language background. It is desirable to select only features that are necessary for the emotion classification while protecting sensitive features. However, there are some features that are necessary for emotion classification. These features may also reveal other demographic traits. In this work, we propose a method to improve inference privacy for sensitive features by injecting noise into the input speech data, but without degrading the SER …

Metadata

publication

ICASSP 2022-2022 IEEE international conference on acoustics, speech and …, 2022

year

2022

publication date

2022/5/23

authors

Tiantian Feng, Hanieh Hashemi, Murali Annavaram, Shrikanth S Narayanan

link

https://ieeexplore.ieee.org/abstract/document/9747265/

resource_link

https://sail.usc.edu/publications/files/Feng-ICASSP2022.pdf

conference

ICASSP 2022-2022 IEEE international conference on acoustics, speech and signal processing (ICASSP)

pages

7702-7706

publisher

IEEE