# Recent Advances in End-to-End Spoken Language Understanding

arXiv: https://arxiv.org/abs/1909.13332 · HTML: https://mlsanity.com/paper/1909.13332/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2019.

## Built on by (most cited first, 13 of 13)

- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding](https://mlsanity.com/paper/2111.02735/) (2021)
- [LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech](https://mlsanity.com/paper/2104.11462/) (2021)
- [A Large-Scale Evaluation of Speech Foundation Models](https://mlsanity.com/paper/2404.09385/) (2024)
- [SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks](https://mlsanity.com/paper/2212.10525/) (2022)
- [End-to-End Spoken Language Understanding for Generalized Voice Assistants](https://mlsanity.com/paper/2106.09009/) (2021)
- [A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog](https://mlsanity.com/paper/2207.13211/) (2022)
- [FANS: Fusing ASR and NLU for on-device SLU](https://mlsanity.com/paper/2111.00400/) (2021)
- [Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning](https://mlsanity.com/paper/2104.10357/) (2021)
- [Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech](https://mlsanity.com/paper/2105.04719/) (2021)
- [Characterizing the adversarial vulnerability of speech self-supervised learning](https://mlsanity.com/paper/2111.04330/) (2021)
- [E2E Spoken Entity Extraction for Virtual Agents](https://mlsanity.com/paper/2302.10186/) (2023)
- [Leveraging Cross-Lingual Transfer Learning in Spoken Named Entity Recognition Systems](https://mlsanity.com/paper/2307.01310/) (2023)

## Abstract (arXiv)

This work investigates spoken language understanding (SLU) systems in the scenario when the semantic information is extracted directly from the speech signal by means of a single end-to-end neural network model. Two SLU tasks are considered: named entity recognition (NER) and semantic slot filling (SF). For these tasks, in order to improve the model performance, we explore various techniques including speaker adaptation, a modification of the connectionist temporal classification (CTC) training criterion, and sequential pretraining.