Evaluation of a small-footprint text and language independent speaker recognition system on forensic data

In this paper we evaluate on a forensic task our text and language independent speaker recognition system, characterized by modest memory requirements and robustness to environment noise. Noise robustness is achieved by employing a Kalman filter-based sequential interacting multiple models (SIMM) algorithm. The evaluation data was provided by the Netherlands Forensic Institute (NFI) and consisted of telephone conversations in four different languages gathered in real police investigations. The results of NFI evaluation show that our small-footprint system provides competitive equal error rates (EER) for the class of text independent systems operating on telephone speech with strong channel mismatch.