  We present an approach to tackle the speaker recognition problem using
Triplet Neural Networks. Currently, the $i$-vector representation with
probabilistic linear discriminant analysis (PLDA) is the most commonly used
technique to solve this problem, due to high classification accuracy with a
relatively short computation time. In this paper, we explore a neural network
approach, namely Triplet Neural Networks (TNNs), to built a latent space for
different classifiers to solve the Multi-Target Speaker Detection and
Identification Challenge Evaluation 2018 (MCE 2018) dataset. This training set
contains $i$-vectors from 3,631 speakers, with only 3 samples for each speaker,
thus making speaker recognition a challenging task. When using the train and
development set for training both the TNN and baseline model (i.e., similarity
evaluation directly on the $i$-vector representation), our proposed model
outperforms the baseline by 23%. When reducing the training data to only using
the train set, our method results in 309 confusions for the Multi-target
speaker identification task, which is 46% better than the baseline model. These
results show that the representational power of TNNs is especially evident when
training on small datasets with few instances available per class.
