Toward Fail-Safe Speaker Recognition: Trial-Based Calibration with a Reject Option

• Autores: Ferrer, Luciana; Nandwana, Mahesh Kumar; McLaren, Mitchell; Castan, Diego; Lawson, Aaron
• Año de publicación: 2019
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: FORENSIC VOICE COMPARISON; SPEAKER RECOGNITION; TRIAL-BASED CALIBRATION
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

The output scores of most of the speaker recognition systems are not directly interpretable as stand-alone values. For this reason, a calibration step is usually performed on the scores to convert them into proper likelihood ratios, which have a clear probabilistic interpretation. The standard calibration approach transforms the system scores using a linear function trained using data selected to closely match the evaluation conditions. This selection, though, is not feasible when the evaluation conditions are unknown. In previous work, we proposed a calibration approach for this scenario called trial-based calibration (TBC). TBC trains a separate calibration model for each test trial using data that is dynamically selected from a candidate training set to match the conditions of the trial. In this work, we extend the TBC method, proposing: 1) a new similarity metric for selecting training data that result in significant gains over the one proposed in the original work; 2) a new option that enables the system to reject a trial when not enough matched data are available for training the calibration model; and 3) the use of regularization to improve the robustness of the calibration models trained for each trial. We test the proposed algorithms on a development set composed of several conditions and on the Federal Bureau of Investigation multi-condition speaker recognition dataset, and we demonstrate that the proposed approach reduces calibration loss to values close to 0 for most of the conditions when matched calibration data are available for selection, and that it can reject most of the trials for which relevant calibration data are unavailable.
Fil: Ferrer, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Nandwana, Mahesh Kumar. No especifíca;
Fil: McLaren, Mitchell. No especifíca;
Fil: Castan, Diego. No especifíca;
Fil: Lawson, Aaron. No especifíca;