Suspect-Adapted MAP Estimation of Within-Source Distributions in Generative Likelihood Ratio Estimation

In this paper, a novel suspect-adaptive technique for robust Bayesian forensic speaker recognition via maximum a posteriori (MAP) estimation is presented, which addresses likelihood ratio (LR) computation in limited suspect speech data conditions obtaining good calibration performance. Robustness is achieved by the use of speaker-independent information, adapting it to the specificities of the suspect involved in the process. Thus, this procedure allows the system to weight the relevance of the suspect specificities depending on the amount of suspect data available via MAP estimation. Experimental results show robustness to suspect data scarcity and stable performance for any amount of suspect material. Also, the proposed technique outperforms other previously proposed non-adaptive approaches. Results are presented as discrimination capabilities (DET plots), distributions of LRs (Tippett plots) and expected cost of wrong decisions over any prior or decision cost (C_llr). The use of such evaluation metrics allows us to highlight the importance of LR calibration in the performance of a forensic system