Peer-reviewed research
Optimization of False Acceptance/Rejection Rates and Decision Threshold for End-to-End Text-Dependent Speaker Verification Systems
Victoria Mingote et al., Proceedings of Interspeech 2019, ISCA Archive, 2019
Read the originalRead on October 17, 2026
- What it is
- A conference paper from the Interspeech 2019 proceedings on speaker verification. It proposes training a neural speaker verification system so that it manages the decision threshold between false rejections and false acceptances directly, and tests this on a text-dependent benchmark.
- How much weight it carries
- Peer-reviewed and published by ISCA, the speech science society that runs Interspeech. It reports results on one research dataset (RSR2015) with a 2019 system, so the numbers do not describe current products. It is cited here only for how the threshold trade-off is framed.
Facts taken from this source
- In a 2019 Interspeech paper on speaker verification, the decision threshold is the setting that controls the ratio between the false rejection rate and the false acceptance rate.
“the threshold used to compute the ratio between the False Rejection Rate (FRR) and the False Acceptance Rate (FAR)” (Abstract)