BACKGROUND Modern speech recognition systems typically include both speech layer and
understanding layer processing to analyze spoken commands or queries provided by a …

In this paper, we study the problem of data augmentation for language understanding in task-
oriented dialogue system. In contrast to previous work which augments an utterance without …

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 …

Prior work in this domain has focused on modeling errors at the phonetic level, while using a
lexicon to convert the phones to words, usually accompanied by an FST Language model …

BACKGROUND The manner in which humans interact with computing devices is rapidly
evolving and has reached the point where human users can access services and resources …

Word vector representations are a crucial part of natural language processing (NLP) and
human computer interaction. In this paper, we propose a novel word vector representation …

Automatic speech recognition (ASR) systems often make unrecoverable errors due to
subsystem pruning (acoustic, language and pronunciation models); for example, pruning …

We propose a novel technique for adapting text-based statistical machine translation to deal
with input from automatic speech recognition in spoken language translation tasks. We …

Documenting endangered languages supports the historical preservation of diverse
cultures. Automatic speech recognition (ASR), while potentially very useful for this task, has …

In the spoken language translation pipeline, machine translation systems that are trained
solely on written bitexts are often unable to recover from speech recognition errors due to …