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 …

This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …

With the development of continuous speech recognition technology, users have put forward
higher requirements in terms of speech recognition accuracy. Low-resource speech …

This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …

We train grapheme-based acoustic models for speech recognition using a hierarchical
recurrent neural network architecture with connectionist temporal classification (CTC) loss …

Computer technology has been used for decades in secondary education and foreign
language preparation. Still, attempts to incorporate technology have presented educators …

For decades, context-dependent phonemes have been the dominant sub-word unit for
conventional acoustic modeling systems. This status quo has begun to be challenged …

We present an analysis of diacritic recognition performance in Arabic Automatic Speech
Recognition (ASR) systems. As most existing Arabic speech corpora do not contain all …

This paper aims at determining the best way to exploit the phonological properties of the
Arabic language in order to improve the performance of the speech recognition system. One …

Speech recognition systems normally use handcrafted pronunciation lexicons designed by
linguistic experts. Building and maintaining such a lexicon is expensive and time …