# LuxSQA: Ask Me in Luxembourgish with TTS-Augmented Spoken Question Answering

Source: [LuxSQA: Ask Me in Luxembourgish with TTS-Augmented Spoken Question Answering](<https://www.sota2.com/research/paper/6a4c87f885dba69fef8b2540>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-07-08T05:00:04.941Z

Authors: Nina Hosseini-Kivanani, Marco Matassoni, Alessio Brutti

Published: 2026-07-02T21:00:52.000Z

## Abstract

Spoken Question Answering (SQA) remains largely focused on high-resource languages and carefully recorded speech, limiting the reach of speech-LLM methods in low-resource settings. This paper investigates whether text-to-speech (TTS) can provide task-specific training data for Luxembourgish SQA without requiring a large human-recorded QA corpus. Starting from existing text-based QA resources, we translate questions into Luxembourgish, synthesize spoken questions with multiple TTS systems, and pair them with textual answers. We train a parameter-efficient SLAM-style architecture that connects a frozen Whisper encoder to frozen multilingual LLM backends through a learned projector and LoRA adapters. We compare MMS-TTS, Qwen3-TTS, and OmniVoice variants, including single-source corpora of about 48k questions and a 4TTS multi-source mix of approximately 230k questions. Evaluation on LLAMA-LB-Test with two real Luxembourgish speaker conditions shows that multi-source and voice-design-based synthetic training configurations yield the strongest SQA performance. The results also show that no-reference TTS quality scores do not monotonically predict downstream QA performance, indicating that synthetic speech must be evaluated as task-specific training data rather than only as natural-sounding audio.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2607.02763>)

[PDF](<https://arxiv.org/pdf/2607.02763.pdf>)