Feature-based likelihood ratios for speaker recognition from linguistically-constrained formant-based i-vectors

In this paper, a probabilistic model is introduced to obtain feature-based likelihood ratios from linguistically-constrained formant-based i-vectors in a NIST SRE task. Linguisticallyconstrained formant-based i-vectors summarize both the static and dynamic information of formant frequencies in the occurrences of a given linguistic unit in a speech recording. In this work, a two-covariance model is applied to these “higher-level” features in order to obtain likelihood ratios through a probabilistic framework. While the performance of the individual linguistically-constrained systems are not comparable to that of a state-of-the-art cepstral-based system, calibration loss is low enough, providing informative likelihood ratios that can be directly used, for instance, in forensic applications. Furthermore, this procedure avoids the need for further calibration steps, which usually require additional datasets. Finally, the fusion of several linguistically-constrained systems greatly improves the overall performance, achieving very remarkable results for a system solely based on formant features. Testing on the English-only trials of the core condition of the NIST 2006 SRE (and using only NIST SRE 2004 and 2005 data for background and development, respectively), we report equal error rates of 8.47% and 9.88% for male and female speakers respectively, using only formant frequencies as speaker discriminative information.