Machine learning applied to speech language understanding : towards semi-supervised and self-evolving systems
type: Dataset
description: Two wide research fields named Speech Recognition and Machine Learning meet with the Automatic Speech Language Understanding. One of the main problems in this domain is to obtain a sufficient corpus to train an efficient statistical model. Such speech corpora need a lot of human involvement to transcript and semantically annotate them. Their production cost is therefore quite high and they are difficultly available.This thesis mainly aims at reducing the need of human intervention in two ways: firstly, reducing the amount of corpus needed to build a model thanks to some semi-supervised learning methods (Self-Training, Co-Training and Active-Learning); And lastly, using the answers of the system end-user to improve the comprehension model.This last point addresses another problem related to automatic speech understanding systems: the need to adapt their models to the fluctuation of end-user habits or to the modification of the services list offered by the system
landing page: https://theses.hal.science/tel-00858980
identifier: NNT: 2012AVIG0180
keyword: active-learning
keyword: apprentissage-automatique
keyword: apprentissage-en-ligne
keyword: apprentissage-semi-supervise
keyword: co-training
keyword: comprehension-de-la-parole
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: lazy-oracle
keyword: lazy-perceptron
keyword: machine-learning
keyword: oracle-partiel
keyword: self-training
keyword: semi-supervised-learning
keyword: speech-language
keyword: theses
keyword: understanding
issued: 2026-05-09T20:31:48.549768
modified: 2026-05-09T20:31:48.549774
language: fr
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Machine learning applied to speech language understanding : towards semi-supervised and self-evolving systems
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00858980
issued: 2026-05-09T20:31:48.562840
modified: 2026-05-09T20:31:48.531632