Lebenchmark: A reproducible framework for assessing self-supervised representation learning from speech
Self-Supervised Learning (SSL) using huge unlabeled data has been successfully explored
for image and natural language processing. Recent works also investigated SSL from …
for image and natural language processing. Recent works also investigated SSL from …
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. In the present work, we use wav2vec …
continuous representations of audio and text content. In the present work, we use wav2vec …
Task agnostic and task specific self-supervised learning from speech with lebenchmark
Self-Supervised Learning (SSL) has yielded remarkable improvements in many different
domains including computer vision, natural language processing and speech processing by …
domains including computer vision, natural language processing and speech processing by …
LeBenchmark 2.0: A standardized, replicable and enhanced framework for self-supervised representations of French speech
Self-supervised learning (SSL) is at the origin of unprecedented improvements in many
different domains including computer vision and natural language processing. Speech …
different domains including computer vision and natural language processing. Speech …
End-to-end speech emotion recognition: challenges of real-life emergency call centers data recordings
Recognizing a speaker's emotion from their speech can be a key element in emergency call
centers. End-to-end deep learning systems for speech emotion recognition now achieve …
centers. End-to-end deep learning systems for speech emotion recognition now achieve …
Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations
The goal of our research is to automatically retrieve the satisfaction and the frustration in real-
life call-center conversations. This study focuses an industrial application in which the …
life call-center conversations. This study focuses an industrial application in which the …
Multi-corpus experiment on continuous speech emotion recognition: convolution or recurrence?
Extraction of semantic information from real-life speech, such as emotions, is a challenging
task that has grown in popularity over the last few years. Recently, emotion processing in …
task that has grown in popularity over the last few years. Recently, emotion processing in …
Prediction of User Request and Complaint in Spoken Customer-Agent Conversations
N Lackovic, C Montacié, G Lalande… - arxiv preprint arxiv …, 2022 - arxiv.org
We present the corpus called HealthCall. This was recorded in real-life conditions in the call
center of Malakoff Humanis. It includes two separate audio channels, the first one for the …
center of Malakoff Humanis. It includes two separate audio channels, the first one for the …
Evaluating Emotional Nuances in Dialogue Summarization
Automatic dialogue summarization is a well-established task that aims to identify the most
important content from human conversations to create a short textual summary. Despite …
important content from human conversations to create a short textual summary. Despite …
End-to-End Continuous Speech Emotion Recognition in Real-life Customer Service Call Center Conversations
Speech Emotion recognition (SER) in call center conversations has emerged as a valuable
tool for assessing the quality of interactions between clients and agents. In contrast to …
tool for assessing the quality of interactions between clients and agents. In contrast to …