Automatic emotions recognition during call center conversations
type: Dataset
description: Automatic emotion recognition in speech is a relatively recent research subject in the field of natural language processing considering that the subject has been proposed for the first time about ten years ago. This subject is nowadays the object of much attention, not only in academia but also in industry, thank to the increased models performance and system reliability. The first studies were based on acted data and non spontaneous speech. Up until now, most experiments carried out by the research community on emotions were realized pre-segmented sequences and with a unique speaker and not on spontaneous speech with several speaker. With this methodology the models built on acted data are hardly usable on data collected in natural context The studies we present in this thesis are based on call center’s conversation with about 1620 hours of dialogs and with at least two human speakers (a commercial agent and a client) for each conversation. Our aim is the detection, via emotional expression, of the client satisfaction.In the first part of this work we present the results we obtained from models using only acoustic or linguistic features for emotion detection. We show that to obtain correct results an approach taking into account only one of these features type is not enough. To overcome this problem we propose the combination of three type of features (acoustic, lexical and semantic). We show that the use of models with features fusion allows higher score for the recognition step in all case compared to the model using only acoustic features. This gain is also obtained if we use an approach without manual pre-processing (automatic segmentation of conversation, transcriptions based on automatic speech recognition).In the second part of our study we notice that even if models based on features combination are relevant for emotion detection the amount of data we use in our training set is too small if we used it on large amount of data test. To overcome this problem w…
identifier: NNT: 2012PA112128
issued: 2026-06-03T10:03:58.012263
language: fr
modified: 2026-06-03T10:03:58.012268
contact point: Organization
  fn: CCSD
keyword: apprentissage-semi-supervise
keyword: detection-demotions
keyword: emotions-detection
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: natural-language-processing
keyword: reconnaissance-de-la-parole
keyword: semi-supervised-learning
keyword: speech-processing
keyword: theses
keyword: traitement-automatique-des-langues
landing page: https://theses.hal.science/tel-00758650

Automatic emotions recognition during call center conversations
type: Distribution
format: HTML
issued: 2026-06-03T10:03:58.124673
modified: 2026-06-03T10:03:57.964470
access URL: https://theses.hal.science/tel-00758650

test_moissonnage_selune
type: Agent