LUMO (Lightweight Unified Multilingual Orchestrator): A Privacy Preserving Offline Voice Assistant
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Computer Science > Machine Learning
arXiv:2609.30692 (cs)
[Submitted on 25 Sep 2026]
Title:LUMO (Lightweight Unified Multilingual Orchestrator): A Privacy Preserving Offline Voice Assistant
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Abstract:Reliable voice interaction is essential in environments with limited internet connectivity and strong privacy. However, most existing voice assistants depend on cloud-based services, which leads to latency issues, dependency on internet access, and privacy vulnerabilities. This research presents LUMO (Lightweight Unified Multilingual Orchestrator), a privacy preserving offline voice assistant designed for edge computing environments. This system integrates local Automatic Speech Recognition (ASR), locally deployed quantized Large Language Model (LLM), and Text-to-Speech (TTS) synthesis into a fully offline pipeline running on a Raspberry Pi 5 with 8 GB RAM.
To enable efficient operation on resource constrained hardware, the language model is compressed using 4-bit GGUF quantization, which reduces memory usage while preserving practical conversational capability. Existing edge based voice assistants Mycroft provides partial offline functionality without a generative LLM, with an approximate latency of ~5 s and power consumption of ~12 W, while Rhasspy supports full offline operation but lacks generative capabilities, with ~3 s latency and ~11 W power usage. In contrast, LUMO achieves a Word Error Rate (WER) of 6.8% for short English utterances in low noise conditions, an end-to-end response latency of 2.0-4.0 s, and a lower peak power consumption of approximately 9.0 W. The system also achieves effective offline recognition for Bangla speech, supporting multilingual accessibility in low resource settings. By operating entirely offline, LUMO provides strong data privacy, reduced need for cloud connectivity, and suitability for privacy sensitive edge execution such as rural healthcare, education, and disaster response scenarios.
| Comments: | 6 pages, 8 figures, 9 tables. Conference version prepared for IEEE 3rd International Conference on Computing, Applications and Systems (COMPAS 2026), 9-10 October 2026, University of Dhaka, Bangladesh |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.30692 [cs.LG] |
| (or arXiv:2609.30692v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.30692
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Md Mehedi Hasan Naeem [view email][v1] Fri, 25 Sep 2026 01:57:42 UTC (3,536 KB)
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