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

The end-to-end lattice-free maximum mutual information (LF-MMI) approach has recently
been shown to be beneficial for automatic speech recognition (ASR) in general. More …

In this paper, a self-supervised learning pre-trained model is proposed and successfully
applied in language identification task (LID). A Transformer encoder is employed and multi …

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 …

Developing a practical speech recognizer for a low resource language is challenging, not
only because of the (potentially unknown) properties of the language, but also because test …

The construction of pronunciation dictionaries relies on high-quality and extensive training
data in data-driven way. However, the manual annotation of corpus for this purpose is both …

Rapid deployment of automatic speech recognition (ASR) in new languages, with very
limited data, is of great interest and importance for intelligence gathering, as well as for …

State-of-the-art acoustic models for Automatic Speech Recognition (ASR) are based on
Hidden Markov Models (HMM) and Deep Neural Networks (DNN) and often require …