Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

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 …

Techniques for multi-lingual and cross-lingual speech recognition can help in low resource
scenarios, to bootstrap systems and enable analysis of new languages and domains. End-to …

Deep neural networks (DNNs) have achieved great success in acoustic modeling for speech
recognition. However, DNNs with sigmoid neurons may suffer from the vanishing gradient …

This paper examines the impact of multilingual (ML) acoustic representations on Automatic
Speech Recognition (ASR) and keyword search (KWS) for low resource languages in the …

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 …

The acoustic model trained using the knowledge from the shared hidden layer (SHL) model
outperforms the model trained only by using the target language, especially under low …