Logo Lanfrica
  • 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.

Investigation of Automatic Speech Recognition Systems via the Multilingual Deep Neural Network Modeling Methods for a Very Low-Resource Language, Chaha

Domaine:

natural language processing
Créateur:
TesJunTul
Éditeur:
Sci
Hôte:

Visit

doi.org

Tasks

automatic speech recognitionspeech processing

Languages

Sebat Bet Gurage

Licenses

http://creativecommons.org/licenses/by/4.0/

Similaires

Speech recognition system based on deep neural network acoustic modeling for low resourced language-AmharicInvestigation of Various Hybrid Acoustic Modeling Units via a Multitask Learning and Deep Neural Network Technique for LVCSR of the Low-Resource Language, AmharicGenerative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech RecognitionAdaptive Activation Network For Low Resource Multilingual Speech RecognitionSMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech RecognitionLexical modeling for the development of Amharic automatic speech recognition systems

Speech recognition system based on deep neural network acoustic modeling for low resourced language-Amharic

Investigation of Various Hybrid Acoustic Modeling Units via a Multitask Learning and Deep Neural Network Technique for LVCSR of the Low-Resource Language, Amharic

Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech Recognition

It is important to transcribe and archive speech data of endangered languages for preserving heritag

Adaptive Activation Network For Low Resource Multilingual Speech Recognition

Low resource automatic speech recognition (ASR) is a useful but thorny task, since deep learning ASR

SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition

Automatic Speech Recognition (ASR) models demonstrate outstanding performance on high-resource langu

Lexical modeling for the development of Amharic automatic speech recognition systems