Error correction techniques have been used to refine the output sentences from automatic
speech recognition (ASR) models and achieve a lower word error rate (WER) than original …

Off-the-shelf pre-trained Automatic Speech Recognition (ASR) systems are an increasingly
viable service for companies of any size building speech-based products. While these ASR …

Modern Automatic Speech Recognition (ASR) systems can achieve high performance in
terms of recognition accuracy. However, a perfectly accurate transcript still can be …

Learning the underlying patterns in data goes beyond instance-based generalization to
external knowledge represented in structured graphs or networks. Deep learning that …

Error correction in automatic speech recognition (ASR) aims to correct those incorrect words
in sentences generated by ASR models. Since recent ASR models usually have low word …

Abstract Domain Adaptation for Automatic Speech Recognition (ASR) error correction via
machine translation is a useful technique for improving out-of-domain outputs of pre-trained …

In the field of Automatic Speech Recognition (ASR), Grammatical Error Correction (GEC)
can be used to correct errors in recognition results of ASR systems and whereby it further …

Hallucinations are a type of output error produced by deep neural networks. While this has
been studied in natural language processing, they have not been researched previously in …

Language models (LMs) have long been used to improve results of automatic speech
recognition (ASR) systems, but they are unaware of the errors that ASR systems make. Error …

Even though Automatic Speech Recognition (ASR) systems significantly improved over the
last decade, they still introduce a lot of errors when they transcribe voice to text. One of the …