Concept

Langues en Inde

Publications associées (20)

The spatial and economic footprint of the coal industry on rural livelihoods in Jharkhand, India

The socio-economic dimensions of coal mining in India form a complex picture of inter -linked livelihoods and economic dependencies. In this article, we contribute to sharpening this understanding through an analysis of household reliance on coal -related ...

London2024

Slim: Explicit Slot-Intent Mapping With Bert For Joint Multi-Intent Detection And Slot Filling

Boi Faltings, Fei Mi, Fengyu Cai, Wanhao Zhou

Utterance-level intent detection and token-level slot filling are two key tasks for spoken language understanding (SLU) in task-oriented systems. Most existing approaches assume that only a single intent exists in an utterance. However, there are often mul ...

IEEE2022

PARSINLU: A Suite of Language Understanding Challenges for Persian

Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the wid ...

2021

ODIANLP's Participation in WAT2020

Petr Motlicek

This paper describes the team (“ODI-ANLP”)’s submission to WAT 2020. We have participated in the English→HindiMultimodal task and Indic task. We have used the state-of-the-art Transformer model for the translation task and Incep-tionResNetV2 for the Hindi ...

ACL2020

HMM-based Approaches to Model Multichannel Information in Sign Language inspired from Articulatory Features-based Speech Processing

Marzieh Razavi, Sandrine Tornay, Necati Cihan Camgöz

Sign language conveys information through multiple channels, such as hand shape, hand movement, and mouthing. Modeling this multi-channel information is a highly challenging problem. In this paper, we elucidate the link between spoken language and sign lan ...

IEEE2019

Multilingual bottleneck features for subword modeling in zero-resource languages

Enno Hermann

How can we effectively develop speech technology for languages where no transcribed data is available? Many existing approaches use no annotated resources at all, yet it makes sense to leverage information from large annotated corpora in other languages, f ...

2018

Towards Weakly Supervised Acoustic Subword Unit Discovery and Lexicon Development Using Hidden Markov Models

Ramya Rasipuram, Marzieh Razavi

Developing a phonetic lexicon for a language requires linguistic knowledge as well as human effort, which may not be available, particularly for under-resourced languages. An alternative to development of a phonetic lexicon is to automatically derive subwo ...

Idiap2017

On Modeling the Synergy Between Acoustic and Lexical Information for Pronunciation Lexicon Development

Marzieh Razavi

State-of-the-art automatic speech recognition (ASR) and text-to-speech systems require a pronunciation lexicon that maps each word to a sequence of phones. Manual development of lexicons is costly as it needs linguistic knowledge and human expertise. To fa ...

EPFL2017

SyReC: A hardware description language for the specification and synthesis of reversible circuits

Mathias Soeken

Although researchers and engineers originally focused on a preponderantly irreversible computing paradigm, alternative models receive more and more attention. Reversible computation is a promising example which has applications in many emerging technologie ...

2016

Excluded Linguistic Communities and the Production of an Inclusive Multilingual Digital Language Infrastructure

Martin Benjamin

The consequence of linguistic digital exclusion is the inability of billions of people to access vital knowledge and economic resources that contribute to prosperity in an era of globalization. However, rectifying linguistic inequity is mostly absent from ...

2015

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search