  We study the individuality of the human voice with respect to a widely used
feature representation of speech utterances, namely, the i-vector model. As a
first step toward this goal, we compare and contrast uniqueness measures
proposed for different biometric modalities. Then, we introduce a new
uniqueness measure that evaluates the entropy of i-vectors while taking into
account speaker level variations. Our measure operates in the discrete feature
space and relies on accurate estimation of the distribution of i-vectors.
Therefore, i-vectors are quantized while ensuring that both the quantized and
original representations yield similar speaker verification performance.
Uniqueness estimates are obtained from two newly generated datasets and the
public VoxCeleb dataset. The first custom dataset contains more than one and a
half million speech samples of 20,741 speakers obtained from TEDx Talks videos.
The second one includes over twenty one thousand speech samples from 1,595
actors that are extracted from movie dialogues. Using this data, we analyzed
how several factors, such as the number of speakers, number of samples per
speaker, sample durations, and diversity of utterances affect uniqueness
estimates. Most notably, we determine that the discretization of i-vectors does
not cause a reduction in speaker recognition performance. Our results show that
the degree of distinctiveness offered by i-vector-based representation may
reach 43-70 bits considering 5-second long speech samples; however, under less
constrained variations in speech, uniqueness estimates are found to reduce by
around 30 bits. We also find that doubling the sample duration increases the
distinctiveness of the i-vector representation by around 20 bits.
