# Spoken Language Understanding on the Edge

arXiv: https://arxiv.org/abs/1810.12735 · HTML: https://mlsanity.com/paper/1810.12735/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2018.

## Built on by (most cited first, 27 of 27)

- [MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages](https://mlsanity.com/paper/2204.08582/) (2022)
- [BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing](https://mlsanity.com/paper/2309.00916/) (2023)
- [Self-Supervised Speech Representations are More Phonetic than Semantic](https://mlsanity.com/paper/2406.08619/) (2024)
- [ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet](https://mlsanity.com/paper/2111.14706/) (2021)
- [A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding](https://mlsanity.com/paper/2211.05869/) (2022)
- [Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers](https://mlsanity.com/paper/2104.01604/) (2021)
- [Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding](https://mlsanity.com/paper/2106.15065/) (2021)
- [UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions](https://mlsanity.com/paper/2310.02973/) (2023)
- [On the Evaluation of Speech Foundation Models for Spoken Language Understanding](https://mlsanity.com/paper/2406.10083/) (2024)
- [Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible Inputs](https://mlsanity.com/paper/2104.05752/) (2021)
- [Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants](https://mlsanity.com/paper/2405.19342/) (2024)
- [Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems](https://mlsanity.com/paper/2104.01287/) (2021)
- [On Building Spoken Language Understanding Systems for Low Resourced Languages](https://mlsanity.com/paper/2205.12818/) (2022)
- [Multimodal Audio-textual Architecture for Robust Spoken Language Understanding](https://mlsanity.com/paper/2306.06819/) (2023)
- [Tokenwise Contrastive Pretraining for Finer Speech-to-BERT Alignment in End-to-End Speech-to-Intent Systems](https://mlsanity.com/paper/2204.05188/) (2022)
- [Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text models](https://mlsanity.com/paper/2206.14589/) (2022)
- [Quantization-Aware and Tensor-Compressed Training of Transformers for Natural Language Understanding](https://mlsanity.com/paper/2306.01076/) (2023)
- [MSNER: A Multilingual Speech Dataset for Named Entity Recognition](https://mlsanity.com/paper/2405.11519/) (2024)
- [Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model](https://mlsanity.com/paper/2406.12317/) (2024)
- [Safeguarding Privacy in Edge Speech Understanding with Tiny Foundation Models](https://mlsanity.com/paper/2502.01649/) (2025)
- [Low-bit Shift Network for End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2207.07497/) (2022)
- [Introducing Visual Scenes and Reasoning: A More Realistic Benchmark for Spoken Language Understanding](https://mlsanity.com/paper/2511.19005/) (2025)
- [A Study of Different Ways to Use The Conformer Model For Spoken Language Understanding](https://mlsanity.com/paper/2204.03879/) (2022)
- [Jaco: An Offline Running Privacy-aware Voice Assistant](https://mlsanity.com/paper/2209.07775/) (2022)
- [Bidirectional Representations for Low Resource Spoken Language Understanding](https://mlsanity.com/paper/2211.14320/) (2022)
- [Digits micro-model for accurate and secure transactions](https://mlsanity.com/paper/2402.01931/) (2024)
- ["KAN you hear me?" Exploring Kolmogorov-Arnold Networks for Spoken Language Understanding](https://mlsanity.com/paper/2505.20176/) (2025)

## Abstract (arXiv)

We consider the problem of performing Spoken Language Understanding (SLU) on small devices typical of IoT applications. Our contributions are twofold. First, we outline the design of an embedded, private-by-design SLU system and show that it has performance on par with cloud-based commercial solutions. Second, we release the datasets used in our experiments in the interest of reproducibility and in the hope that they can prove useful to the SLU community.