English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Pre-training for feature extraction is an increasingly studied approach toget better continuous representations of audio and text content. In the presentwork, we use wav2vec and camemBERT as self-supervised learned models torepresent our data in order to perform continuous emotion recognition fromspeech (SER) on AlloSat, a large French emotional database describing thesatisfaction dimension, and on the state of the art corpus SEWA focusing onvalence, arousal and liking dimensions. To the authors' knowledge, this paperpresents the first study showing that the joint use of wav2vec and BERT-likepre-trained features is very relevant to deal with continuous SER task, usuallycharacterized by a small amount of labeled training data. Evaluated by thewell-known concordance correlation coefficient (CCC), our experiments show thatwe can reach a CCC value of 0.825 instead of 0.592 when using MFCC inconjunction with word2vec word embedding on the AlloSat dataset.

On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition