The polarization of world languages is becoming more and more obvious. Many languages,
mainly endangered languages, are of low-resource attribute due to lack of information. Both …

Neural NLP systems achieve high scores in the presence of sizable training dataset. Lack of
such datasets leads to poor system performances in the case low-resource languages. We …

Abstract Automatic Speech Recognition (ASR) system for children is as important as for
adults since children are more dependent on these systems nowadays, such as computer …

High quality end-to-end speech translation model relies on a large scale of speech-to-text
training data, which is usually scarce or even unavailable for some low-resource language …

Text-to-speech synthesis (TTS) has been used as a data augmentation approach for
automatic speech recognition (ASR), leveraging additional texts for ASR training. However …

In this paper, we present an improved vocal tract length perturbation (VTLP) algorithm as a
data augmentation technique. VTLP is usually accomplished by adjusting the center …

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

Speech recognition of foreign accented (non-native or L2) speech remains a challenge to
the state-of-the-art. The most common approach to address this scenario involves the …

For building a successful automatic speech recognition (ASR) engine large training data is
required. It increases training complexity and become impossible for less resource language …

To addresses the challenge of underwater grouting sleeve corrosion affecting bridge service
life, we propose an innovative inspection approach by integrating a modified ultrasonic …