We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

End-to-end (E2E) automatic speech recognition (ASR) systems often have difficulty
recognizing uncommon words, that appear infrequently in the training data. One promising …

In this paper we propose a novel virtual simulation-pilot engine for speeding up air traffic
controller (ATCo) training by integrating different state-of-the-art artificial intelligence (AI) …

Voice communication between air traffic controllers (ATCos) and pilots is critical for ensuring
safe and efficient air traffic control (ATC). The handling of these voice communications …

Increasing amount of research has shed light on machine perception of audio events, most
of which concerns detection and classification tasks. However, human-like perception of …

End-to-end approaches to automatic speech recognition, such as Listen-Attend-Spell (LAS),
blend all components of a traditional speech recognizer into a unified model. Although this …

In recent years, all-neural, end-to-end (E2E) ASR systems gained rapid interest in the
speech recognition community. They convert speech input to text units in a single trainable …

Contextual information plays a crucial role in speech recognition technologies and
incorporating it into the end-to-end speech recognition models has drawn immense interest …

End-to-end automatic speech recognition (ASR) and large language models, such as
Whisper and GPT-2, have recently been scaled to use vast amounts of training data. Despite …

Text-to-Speech synthesis (TTS) based data augmentation is a relatively new mechanism for
utilizing text-only data to improve automatic speech recognition (ASR) training without …