Few Shot Speaker Recognition using Deep Neural Networks

TL;DR

This paper presents a deep learning approach for speaker recognition with limited training data, utilizing prototypical loss and capsule networks to improve performance in few-shot learning scenarios.

Contribution

It introduces a novel few-shot speaker recognition method combining prototypical loss and capsule networks, enhancing generalization with minimal training samples.

Findings

Proposed method outperforms existing baselines on public datasets.

Capsule networks improve feature embedding quality for few-shot learning.

Auto-encoder enhances generalization of speaker embeddings.

Abstract

The recent advances in deep learning are mostly driven by availability of large amount of training data. However, availability of such data is not always possible for specific tasks such as speaker recognition where collection of large amount of data is not possible in practical scenarios. Therefore, in this paper, we propose to identify speakers by learning from only a few training examples. To achieve this, we use a deep neural network with prototypical loss where the input to the network is a spectrogram. For output, we project the class feature vectors into a common embedding space, followed by classification. Further, we show the effectiveness of capsule net in a few shot learning setting. To this end, we utilize an auto-encoder to learn generalized feature embeddings from class-specific embeddings obtained from capsule network. We provide exhaustive experiments on publicly available datasets and competitive baselines, demonstrating the superiority and generalization ability of the proposed few shot learning pipelines.