Multilingual end-to-end (E2E) models have shown great promise in expansion of automatic
speech recognition (ASR) coverage of the world's languages. They have shown …

With the emergence of deep learning, the performance of automatic speech recognition
(ASR) systems has remarkably improved. Especially for resource-rich languages such as …

Code-switching (CS) in spoken language is where the speech has two or more languages
within an utterance. It is an unsolved issue in automatic speech recognition (ASR) research …

Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively new
direction in speech research. The approach benefits by performing model training without …

In this paper, we proposed to apply meta learning approach for low-resource automatic
speech recognition (ASR). We formulated ASR for different languages as different tasks, and …

Although Automatic Speech Recognition (ASR) systems have achieved human-like
performance for a few languages, the majority of the world's languages do not have usable …

We live in a world where 60% of the population can speak two or more languages fluently.
Members of these communities constantly switch between languages when having a …

Cross-lingual transfer of knowledge from high-resource languages to low-resource
languages is an important research problem in automatic speech recognition (ASR). We …

Low-resource automatic speech recognition (ASR) is challenging, as the low-resource target
language data cannot well train an ASR model. To solve this issue, meta-learning …

Multilingual Automated Speech Recognition (ASR) systems allow for the joint training of
data-rich and data-scarce languages in a single model. This enables data and parameter …