# Jaco: An Offline Running Privacy-aware Voice Assistant

arXiv: https://arxiv.org/abs/2209.07775 · HTML: https://mlsanity.com/paper/2209.07775/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [Spoken Language Understanding on the Edge](https://mlsanity.com/paper/1810.12735/) (2018)
- [Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models](https://mlsanity.com/paper/1910.09463/) (2019)
- [Exploring Transfer Learning For End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2012.08549/) (2020)
- [Scribosermo: Fast Speech-to-Text models for German and other Languages](https://mlsanity.com/paper/2110.07982/) (2021)

## Abstract (arXiv)

With the recent advance in speech technology, smart voice assistants have been improved and are now used by many people. But often these assistants are running online as a cloud service and are not always known for a good protection of users' privacy. This paper presents the architecture of a novel voice assistant, called Jaco, with the following features: (a) It can run completely offline, even on low resource devices like a RaspberryPi. (b) Through a skill concept it can be easily extended. (c) The architectural focus is on protecting users' privacy, but without restricting capabilities for developers. (d) It supports multiple languages. (e) It is competitive with other voice assistant solutions. In this respect the assistant combines and extends the advantages of other approaches.