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

Abstract

Speaker recognition is a well established area for research but it mainly focuses on adult speech. Recent work on children’s speech shows that not all the findings from speaker recognition on adult speech are directly applicable on children’s speech. There are a 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 on social media network-ing 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 the i-vector framework using two simulated environments; in a classroom (contains 30 students) and in a school (contains 288 students). The first method is based on the PLDA scoring approach and the second method is based on the cosine similarity measure. Results show that the first method outperforms the second approach 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. focused on both speaker identification and verification for text-independent mode of operation.