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arXiv · 1910.01463

Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks

Kin Wai Cheuk·Balamurali B. T.·Gemma Roig·Dorien Herremans
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Abstract

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.

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Kin Wai Cheuk, Balamurali B. T., Gemma Roig, Dorien Herremans. 2019-10-01. Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks. https://arxiv.org/abs/1910.01463

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