Bulbul: A Dataset for Dialectal Arabic Speech Recognition

2026-08-22

AI Summary

为解决阿拉伯语自动语音识别面临的独特挑战，通过构建包含275名来自11个阿拉伯国家的说话者的多方言数据集BULBUL，并采用两层人工验证确保质量。

Abstract

Arabic automatic speech recognition (ASR) faces unique challenges due to diglossia, extensive regional dialect variation, and limited speech resources. Existing speech datasets often focus on single dialects or large-scale broadcast/web data, leading to trade-offs between linguistic diversity and annotation quality. We present BULBUL, a multi-dialect Arabic ASR dataset collected from 275 speakers in 11 Arab countries. BULBUL includes structured dialect and sub-dialect coverage, as well as recordings of classical Arabic and modern standard Arabic spoken by participants in their native dialectal accents to support accent-aware modeling. The quality of the recordings was ensured through a two-level human verification process. We further benchmark a range of recent ASR systems, establishing strong baselines for modern dialectal and accented Arabic ASR.

Problem

• Arabic automatic speech recognition
• dialect variation
• speech resources

Innovation

• multi-dialect Arabic ASR
• accent-aware modeling
• two-level human verification