A Fuzzy Approach to Mute Sensitive Information in Noisy Audio Conversations

Abstract

Audio conversation between a service seeking customer and an agent are common in a voice based call center (VbCC) and are often recorded either for audit purposes or to enable the VbCC to improve their efficiency. These audio recordings invariably containpersonal information of the customer, often spoken by the customer to confirm their identity to get personalized services from the agent. This private to a person (P2aP) information is the recordings is a serious concern from the GDPR perspective and can lead to identity the ftamong other things. In this paper, we propose a robust framework that enables us to reliably spot the P2aP information in the audio and automatically mute it. The main contribution of this paper is the proposal of a novelfuzzy criteria which by design allows for reduced false alarms and at the same time increases the accuracy of the muting process even when the speech to text conversion process is erroneous. Evaluation on real call center conversations demonstrates the reliability of the proposed approach.