The paper introduces Aslema, a system for the NADI 2026 Shared Task 5, which includes intent recognition and slot filling. The authors evaluate four omni LLMs in zero‑shot and fine‑tuned settings, finding that fine‑tuning consistently outperforms zero‑shot inference. They further augment data by generating culturally grounded Tunisian Derja utterances with an LLM and synthetic speech via voice cloning, which improves performance; the final system based on Qwen3‑Omni‑30B achieves 86.8% intent accuracy and 34.7 WER on devtest, ranking 1st in slot filling and 4th in intent recognition on the official test set.
By Tajwaar Shafiq, Hunzalah Hassan Bhatti, Firoj Alam, Shammur Absar Chowdhury
arXiv:2606. 03957v1 Announce Type: cross Abstract: Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data.
By M\'at\'e Gedeon, P\'eter Mihajlik
Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations.
arXiv:2603. 04219v2 Announce Type: replace-cross Abstract: We investigate the use of zero-shot text-to-speech (ZS-TTS) as a data augmentation source for low-resource personalized speech synthesis.
By Youngwon Choi, Jinwoo Oh, Hwayeon Kim, Hyeonyu Kim
Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS systems still depend on audio prompt transcripts at inference time.
arXiv:2603.09215v2 Announce Type: replace
Abstract: Interleaved spoken language models (SLMs) alternately generate text and speech tokens, but decoding at full transformer depth for every step become...
By Hsiao-Ying Huang, Cheng-Han Chiang, Hung-yi Lee