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In recent years identity-vector (i-vector) based speaker verification (SV)systems have become very successful. Nevertheless, environmental noise andspeech duration variability still have a significant effect on degrading theperformance of these systems. In many real-life applications, duration ofrecordings are very short; as a result, extracted i-vectors cannot reliablyrepresent the attributes of the speaker. Here, we investigate the effect ofspeech duration on the performance of three state-of-the-art speakerrecognition systems. In addition, using a variety of available score fusionmethods, we investigate the effect of score fusion for those speakerverification techniques to benefit from the performance difference of differentmethods under different enrollment and test speech duration conditions. Thistechnique performed significantly better than the baseline score fusionmethods.

Incorporation of Speech Duration Information in Score Fusion of Speaker Recognition Systems