Measuring the performance of automatic speech recognition (ASR) systems requires
manually transcribed data in order to compute the word error rate (WER), which is often time …

Recurrent neural networks (RNNs) have recently been applied as the classifiers for
sequential labeling problems. In this paper, deep bidirectional RNNs (DBRNNs) are applied …

This paper addresses the problem of automatic speech recognition (ASR) error detection
and their use for improving spoken language understanding (SLU) systems. In this study, the …

In the last years, deep bidirectional recurrent neural networks (DBRNN) and DBRNN with
long short-term memory cells (DBLSTM) have outperformed the most accurate classifiers for …

Traditional approaches to Mongolian named entity recognition heavily rely on the feature
engineering. Even worse, the complex morphological structure of Mongolian words made …

End-to-end automatic speech recognition (ASR) models aim to learn a generalised speech
representation. However, there are limited tools available to understand the internal …

With the rise of Intelligent Virtual Assistants (IVAs), there is a necessary rise in human effort
to identify conversations containing misunderstood user inputs. These conversations …

Dialectal Arabic speech research suffers from the lack of labelled resources and
standardised orthography. There are three main challenges in dialectal Arabic speech …

Traditional approaches to Named Entity Recognition almost heavily rely on feature
engineering. In this paper, we introduce a kind of bidirectional recurrent neural network with …

This paper addresses errors in continuous Automatic Speech Recognition (ASR) in two
stages: error detection and error type classification. Unlike the majority of research in this …