Transfer learning is a vital technique that generalizes models trained for one setting or task
to other settings or tasks. For example in speech recognition, an acoustic model trained for …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

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 …

Training a conventional automatic speech recognition (ASR) system to support multiple
languages is challenging because the sub-word unit, lexicon and word inventories are …

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

Convolutional neural networks (CNNs) are a standard component of many current state-of-
the-art Large Vocabulary Continuous Speech Recognition (LVCSR) systems. However …

Recently there has been increased interest in Automatic Speech Recognition (ASR) and
Key Word Spotting (KWS) systems for low resource languages. One of the driving forces for …

Recently there has been interest in the approaches for training speech recognition systems
for languages with limited resources. Under the IARPA Babel program such resources have …

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 …

This paper presents a study on multilingual deep neural network (DNN) based acoustic
modeling and its application to new languages. We investigate the effect of phone merging …