We provide a short review on the recent and near-future developments of computational
processing of emotion in the voice, highlighting (a) self-learning of representations moving …

In this work, we explore the use of speaker conditioning sub-networks for speaker
adaptation in a deep neural network (DNN) based speech emotion recognition (SER) …

The use of deep learning (DL) architectures for speech enhancement has recently improved
the robustness of voice applications under diverse noise conditions. These improvements …

In real-world scenarios, dynamic ambient noise often degrades speech quality, highlighting
the need for advanced speech enhancement techniques. Traditional methods, which rely on …

Introduction The effective fusion of text and audio information for categorical and
dimensional speech emotion recognition (SER) remains an open issue, especially given the …

The unprecedented growth of noise pollution over the last decades has raised an always
increasing need for develo** efficient audio enhancement technologies. Yet, the variety of …

Most of the deep learning based speech enhancement methods focus on the modeling of
complicated relationship between the noisy speech and the clean speech without the …

The DeepFilterNet (DFN) architecture was recently proposed as a deep learning model
suited for hearing aid devices. Despite its competitive performance on numerous …

Traditional discrete-time Speech Emotion Recognition (SER) modelling techniques typically
assume that an entire speaker chunk or turn is indicative of its corresponding label. An …

Se desarrolla un estimador de relación señal a ruido (SNR) con el fin de determinar la
calidad de registros sonoros de habla que ingresen a sistemas de reconocimiento …