Comparison of two scoring method within i-vector framework for speaker recognition from children’s speech

Speaker recognition is the well established area for research but it mainly focuses on adult speech. Recent works on children’s speech show that not all the findings from speaker recognition on adult speech are directly applicable on children’s speech. There are variety of applications for speaker recognition from children’s speech, for example it could be used as a safeguard for a child during her/his interactions at social media networking websites. It could also be used as one of the main blocks in automatic tutor systems for educational purposes at schools.
In this research we have evaluated two scoring method for speaker recognition within i-vector framework using two simulated environments; in a classroom (contains 30 students) and in a school (contains 288 students). Results indicates that first method which is based on the PLDA scoring performs better than second method which is based on the cosine similarity measure for speaker recognition in a simulated school, but it is the other way around for the recognition of a child in a classroom in which the second scoring method performs better.