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

Recently, there is increasing interest in multilingual automatic speech recognition (ASR)
where a speech recognition system caters to multiple low resource languages by taking …

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 …

Code-switching in automatic speech recognition (ASR) is an important challenge due to
globalization. Recent research in multilingual ASR shows potential improvement over …

Towards developing high-performing ASR for low-resource languages, approaches to
address the lack of resources are to make use of data from multiple languages, and to …

Recently, end-to-end (E2E) automatic speech recognition (ASR) models have made great
strides and exhibit excellent performance in general speech recognition. However, there …

Multilingual acoustic model training combines data from multiple languages to train an
automatic speech recognition system. Such a system is beneficial when training data for a …

We improve low-resource ASR by integrating the ideas of multilingual training and self-
supervised learning. Concretely, we leverage an International Phonetic Alphabet (IPA) …

Self-supervised model pretraining has recently garnered significant interest. However, using
additional resources in fine-tuning these models has received less attention. We …

The use of phonological features (PFs) potentially allows language-specific phones to
remain linked in training, which is highly desirable for information sharing for multilingual …