Title: Recursive Whitening Transformation for Speaker Recognition on Language Mismatched Condition,
Abstract: Recently in speaker recognition, performance degradation due to the channel
domain mismatched condition has been actively addressed. However, the
mismatches arising from language is yet to be sufficiently addressed. This
paper proposes an approach which employs recursive whitening transformation to
mitigate the language mismatched condition. The proposed method is based on the
multiple whitening transformation, which is intended to remove un-whitened
residual components in the dataset associated with i-vector length
normalization. The experiments were conducted on the Speaker Recognition
Evaluation 2016 trials of which the task is non-English speaker recognition
using development dataset consist of both a large scale out-of-domain (English)
dataset and an extremely low-quantity in-domain (non-English) dataset. For
performance comparison, we develop a state-of- the-art system using deep neural
network and bottleneck feature, which is based on a phonetically aware model.
From the experimental results, along with other prior studies, effectiveness of
the proposed method on language mismatched condition is validated.