  State-of-the-art Deep Learning systems for speaker verification are commonly
based on speaker embedding extractors. These architectures are usually composed
of a feature extractor front-end together with a pooling layer to encode
variable-length utterances into fixed-length speaker vectors. The authors have
recently proposed the use of a Double Multi-Head Self-Attention pooling for
speaker recognition, placed between a CNN-based front-end and a set of fully
connected layers. This has shown to be an excellent approach to efficiently
select the most relevant features captured by the front-end from the speech
signal. In this paper we show excellent experimental results by adapting this
architecture to other different speaker characterization tasks, such as emotion
recognition, sex classification and COVID-19 detection.
