MENASpeechBank: A Reference Voice Bank with Persona-Conditioned Multi-Turn Conversations for AudioLLMs
TL;DR
This work introduces MENASpeechBank, a reference speech bank comprising ~18K high-quality utterances from 124 speakers spanning multiple MENA countries, covering English, Modern Standard Arabic, and regional Arabic varieties, and develops a controllable data pipeline that constructs persona profiles enriched with WVS inspired attributes.
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{https://huggingface.co/datasets/QCRI/MenaSpeechBank)
