Dynamic Recognition of Speakers for Consent Management by Contrastive Embedding Replay
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Voice assistants record sound and can overhear conversations. Thus, a consent management mechanism is desirable such that users can express their wish to be recorded or not. Consent management can be implemented using speaker recognition; users that do not give consent enrol their voice and all further recordings of these users is subsequently not processed. Building speaker recognition based consent management is challenging due to the dynamic nature of the problem, required scalability for large number of speakers, and need for fast speaker recognition with high accuracy. This paper describes a speaker recognition based consent management system addressing the aforementioned challenges. A fully supervised batch contrastive learning is applied to learn the underlying speaker equivariance inductive bias during the training on the set of speakers noting recording dissent. Speakers that do not provide consent are grouped in buckets which are trained continuously. The embeddings are contrastively learned for speakers in their buckets during training and act later as a replay buffer for classification. The buckets are progressively registered during training and a novel multi-strided random sampling of the contrastive embedding replay buffer is proposed. Buckets are contrastively trained for a few steps only in each iteration and replayed for classification progressively leading to fast convergence. An algorithm for fast and dynamic registration and removal of speakers in buckets is described. The evaluation results show that the proposed approach provides the desired fast and dynamic solution for consent management and outperforms existing approaches in terms of convergence speed and adaptive capabilities as well as verification performance during inference.
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