Accueil
Atlas
Analyses
Documentation
Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Creating Spoken Dialog Systems in Ultra-Low Resourced Settings

Domaine:

natural language processing

Type de record:

paperposter

Creating Spoken Dialog Systems in Ultra-Low Resourced Settings

Poster presented at the Deep Learning Indaba 2022 by Moayad Elamin

Tags

deep learning indaba poster dli deep learning indaba 2022

Similaires

Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog SystemsENHANCING TRANSLATION SYSTEMS FOR LOW-RESOURCED SETTINGSZanzibar OpenIVR: An Open-Source Framework for Development of Spoken Dialog SystemsGAIfE: Using GenAI to Improve Literacy in Low-resourced SettingsImproving smoking cessation for vulnerable populations in low-resourced settingsTao–Filipino Neural Machine Translation: Strategies for Ultra–Low-Resource Settings

Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems

Intent Recognition and Slot Identification are crucial components in spoken language understanding (

ENHANCING TRANSLATION SYSTEMS FOR LOW-RESOURCED SETTINGS

There are around 7000 languages that are alive worldwide; among them, only 50-200 languages are well

Zanzibar OpenIVR: An Open-Source Framework for Development of Spoken Dialog Systems

GAIfE: Using GenAI to Improve Literacy in Low-resourced Settings

Illiteracy is a predictor of many negative social and personal outcomes. Illiteracy rates are partic

Improving smoking cessation for vulnerable populations in low-resourced settings

Tobacco smoking remains one of the leading causes of death and disability worldwide. Global estimate

Tao–Filipino Neural Machine Translation: Strategies for Ultra–Low-Resource Settings

Neural Machine Translation (NMT) performance degrades significantly in ultra-low resource settings,