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

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

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

Advanced Convolutional Neural Network-Based Hybrid Acoustic Models for Low-Resource Speech Recognition

Deep neural networks (DNNs) have shown a great achievement in acoustic modeling for speech recogniti

Using different acoustic, lexical and language modeling units for ASR of an under-resourced language - Amharic

State-of-the-art large vocabulary continuous speech recognition systems use mostly phone based acoustic models (AMs) and word based lexical and language models. However, phone based AMs are not efficient in modeling long-term temporal dependencies and the use of wo

Morpheme-based feature-rich language models using Deep Neural Networks for LVCSR of Egyptian Arabic

Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning

Unsupervised machine translation, which utilizes unpaired monolingual corpora as training data, has