MENASpeechBank: A Reference Voice Bank with Persona-Conditioned Multi-Turn Conversations for AudioLLMs

Abstract:Audio large language models (AudioLLMs) enable instruction following over speech and general audio, but progress is limited by the scarcity of diverse, conversational, and instruction-aligned speech--text data. This gap is particularly pronounced for persona-grounded and dialectal interactions, where collecting real multi-speaker recordings remains costly and slow. We introduce MENASpeechBank, a reference speech bank comprising ~18K high-quality utterances from 124 speakers spanning multiple MENA countries, covering English, Modern Standard Arabic (MSA), and regional Arabic varieties. We develop a controllable data pipeline that (i) constructs persona profiles enriched with World Values Survey (WVS) inspired attributes, (ii) defines a taxonomy driven ~5Kconversational scenarios, (iii) matches personas to scenarios via semantic similarity, (iv) generates ~417K role-play conversations with an LLM where the user speaks as the persona and the assistant behaves as a helpful agent, and (v) produces speaker-conditioned user-turn audio (synthetic) from reference recordings to preserve speaker diversity. We evaluate synthetic and human recorded conversations and provide an analysis. We will make the MENASpeechBank available for the community.(\href{this https URL)

Sound (cs.SD); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)

arXiv:2602.07036 [cs.SD]

(or arXiv:2602.07036v2 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2602.07036 Focus to learn more arXiv-issued DOI via DataCite

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