Deep Learning in Speaker Recognition

Ghahabi, Omid; Safari, Pooyan; Hernando, Javier

doi:10.1007/978-3-030-31764-5_6

Cited by 1 publication

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“…These architectures are commonly trained as speaker classifiers in order to be used as speaker embedding extractors. Speaker embeddings are fixed-length vectors extracted from some of the last layers of these Deep Neural Networks (DNNs) [1]. The most known representation is the x-vector [2], which has become state-of-the-art for speaker recognition and has also been used for other tasks such as language and emotion recognition [3,4].…”

Section: Introductionmentioning

confidence: 99%

Double Multi-Head Attention for Speaker Verification

India

Safari

Hernando

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Most state-of-the-art Deep Learning systems for text-independent speaker verification are based on speaker embedding extractors. These architectures are commonly composed of a feature extractor front-end together with a pooling layer to encode variable-length utterances into fixed-length speaker vectors. In this paper we present Double Multi-Head Attention (MHA) pooling, which extends our previous approach based on Self MHA. An additional self attention layer is added to the pooling layer that summarizes the context vectors produced by MHA into a unique speaker representation. This method enhances the pooling mechanism by giving weights to the information captured for each head and it results in creating more discriminative speaker embeddings. We have evaluated our approach with the VoxCeleb2 dataset. Our results show 6.09% and 5.23% relative improvement in terms of EER compared to Self Attention pooling and Self MHA, respectively. According to the obtained results, Double MHA has shown to be an excellent approach to efficiently select the most relevant features captured by the CNN-based front-ends from the speech signal.

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