The speech emotion recognition (SER) field has been active since it became a crucial
feature in advanced Human-Computer Interaction (HCI), and wide real-life applications use …

Speech emotion recognition (SER) as a Machine Learning (ML) problem continues to
garner a significant amount of research interest, especially in the affective computing …

Recent advances in transformer-based architectures have shown promise in several
machine learning tasks. In the audio domain, such architectures have been successfully …

Speech self-supervised models such as wav2vec 2.0 and HuBERT are making revolutionary
progress in Automatic Speech Recognition (ASR). However, they have not been totally …

The performance of spoofing countermeasure systems depends fundamentally upon the use
of sufficiently representative training data. With this usually being limited, current solutions …

Self-supervised pre-trained features have consistently delivered state-of-art results in the
field of natural language processing (NLP); however, their merits in the field of speech …

While Wav2Vec 2.0 has been proposed for speech recognition (ASR), it can also be used for
speech emotion recognition (SER); its performance can be significantly improved using …

The speech representations learned from large-scale unlabeled data have shown better
generalizability than those from supervised learning and thus attract a lot of interest to be …

The remarkable success of transformers in the field of natural language processing has
sparked the interest of the speech-processing community, leading to an exploration of their …

This paper explores applying the wav2vec2 framework to speaker recognition instead of
speech recognition. We study the effectiveness of the pre-trained weights on the speaker …