Home
Atlas
Insights
Docs
Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

The limitations of data perturbation for ASR of learner data in under-resourced languages

Creator:

Jaco Badenhorst

Febe de Wet

Publisher:

IEEE

Host:

Visit

Tasks

automatic speech recognition speech processing

Similar

Gauging the accuracy of automatic speech data harvesting in five under-resourced languagesMultilingual training set selection for ASR in under-resourced Malian languagesSpeech recognition for under-resourced languages: Data sharing in hidden Markov model systemsData-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced LanguagesImproving Speech Recognition for Under-resourced Languages Utilizing Audio-codecs for Data AugmentationMultilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages

Gauging the accuracy of automatic speech data harvesting in five under-resourced languages

Recent research on deep-learning architectures has resulted in substantial improvements in automatic

Multilingual training set selection for ASR in under-resourced Malian languages

We present first speech recognition systems for the two severely under-resourced Malian languages Ba

Speech recognition for under-resourced languages: Data sharing in hidden Markov model systems

For purposes of automated speech recognition in under-resourced environments, t

Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages

Hate speech is a global phenomenon, but most hate speech datasets so far focus on English-language c

Improving Speech Recognition for Under-resourced Languages Utilizing Audio-codecs for Data Augmentation

Presenter: Nirayo Hailu Gebreegziabher, Ingo Siegert, Andreas Nürnberger, MMSP 2020, Virtual Event,

Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages

In this work, we explore the benefits of using multilingual bottleneck features (mBNF) in acoustic m