Spoken language understanding (SLU) tasks have been studied for many decades in the
speech research community, but have not received as much attention as lower-level tasks …

We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-
decoder models for speech data. We induce a pseudo language as a compact discrete …

Unsupervised representation learning for speech processing has matured greatly in the last
few years. Work in computer vision and natural language processing has paved the way, but …

Many self-supervised speech models (S3Ms) have been introduced over the last few years,
improving performance and data efficiency on various speech tasks. However, these …

Many self-supervised speech models (S3Ms) have been introduced over the last few years,
producing performance and data efficiency improvements for a variety of speech tasks …

Collecting sufficient labeled data for spoken language understanding (SLU) is expensive
and time-consuming. Recent studies achieved promising results by using pre-trained …

End-to-end (E2E) spoken language understanding (SLU) is constrained by the cost of
collecting speech-semantics pairs, especially when label domains change. Hence, we …

There has been an increased interest in the integration of pretrained speech recognition
(ASR) and language models (LM) into the SLU framework. However, prior methods often …

The Spoken Language Understanding Evaluation (SLUE) suite of benchmark tasks was
recently introduced to address the need for open resources and benchmarking of complex …

Recent works showed that end-to-end neural approaches tend to become very popular for
spoken language understanding (SLU). Through the term end-to-end, one considers the use …