Self-supervised speech representation learning (speech SSL) has demonstrated the benefit
of scale in learning rich representations for Automatic Speech Recognition (ASR) with …

There are many accurate methods for language identification of long text samples, but
identification of very short strings still presents a challenge. This paper studies a language …

This paper presents the OPUS ecosystem with a focus on the development of open machine
translation models and tools, and their integration into end-user applications, development …

Speech recognition systems trained for morphologically rich languages face the problem of
vocabulary growth caused by prefixes, suffixes, inflections, and compound words. Solutions …

We describe a novel way to implement subword language models in speech recognition
systems based on weighted finite state transducers, hidden Markov models, and deep …

Because in agglutinative languages the number of observed word forms is very high,
subword units are often utilized in speech recognition. However, the proper use of subword …

End-to-End speech recognition has become the center of attention for speech recognition
research, but Hybrid Hidden Markov Model Deep Neural Network (HMM/DNN)-systems …

In this paper, we apply a method of unsupervised morphology learning to a state-of-the-art
phrase-based statistical machine translation (SMT) system. In SMT, words are traditionally …

Speech recognition of a language is a key area in the field of pattern recognition. This paper
presents a comprehensive survey on the speech recognition techniques for non-Indian and …

Neural network language models (NNLMs) have achieved very good performance in large-
vocabulary continuous speech recognition (LVCSR) systems. Because decoding with …