Recent performance gains in speaker verification systems suggest it is now viable to employ these systems in open-set speaker identification applications where an automated decision is passed to a human-in-the-loop for final analysis and decision.
Analysis of Top-K Strategies for Open-Set Speaker Identification Applications
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Recent performance gains in speaker verification systems suggest it is now viable to employ these systems in open-set speaker identification applications where an automated decision is passed to a human-in-the-loop for final analysis and decision. This paper examines the performance for when a speaker verification system expands into the identification domain. Our results indicate that separate thresholds should be adopted for the verification and the speaker identification phases. Furthermore, adopting a "top-k" approach where the best k matches are passed to the analyst for final matching does not greatly improve system detection performance and has a significant impact on overall human workload.