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Found 7 papers, 0 papers with code
On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition
Pre-training for feature extraction is an increasingly studied approach to get better continuous representations of audio and text content.
Pr\'ediction continue de la satisfaction et de la frustration dans des conversations de centre d'appels (AlloSat : A New Call Center French Corpus for Affect Analysis)
AlloSat regroupe 303 conversations pour un total d{'}environ 37 heures d{'}audio, toutes enregistr{\'e}es dans des environnements r{\'e}els, collect{\'e}es par Allo-Media (une soci{\'e}t{\'e} sp{\'e}cialis{\'e}e dans l{'}analyse automatique d{'}appels).
AlloSat: A New Call Center French Corpus for Satisfaction and Frustration Analysis
We present a new corpus, named AlloSat, composed of real-life call center conversations in French that is continuously annotated in frustration and satisfaction.
Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks
In this paper, we present improvements made to the TED-LIUM corpus we released in 2012.
TED-LIUM: an Automatic Speech Recognition dedicated corpus
This paper presents the corpus developed by the LIUM for Automatic Speech Recognition (ASR), based on the TED Talks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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