ASMDD: Arabic Speech Mispronunciation Detection Dataset
Salah A. Aly, Abdelrahman Salah, Hesham M. Eraqi
TL;DR
This paper introduces ASMDD, the largest annotated dataset of Egyptian children's Arabic speech for mispronunciation detection, focusing on the top 100 frequently used words.
Contribution
It provides a comprehensive, expert-annotated dataset specifically designed for Arabic speech mispronunciation detection in Egyptian children.
Findings
Largest dataset of its kind for Arabic speech mispronunciation detection
Includes detailed annotations by expert listeners
Focuses on commonly used words in Egyptian Arabic
Abstract
The largest dataset of Arabic speech mispronunciation detections in Egyptian dialogues is introduced. The dataset is composed of annotated audio files representing the top 100 words that are most frequently used in the Arabic language, pronounced by 100 Egyptian children (aged between 2 and 8 years old). The dataset is collected and annotated on segmental pronunciation error detections by expert listeners.
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Taxonomy
TopicsSpeech Recognition and Synthesis · Speech and Audio Processing · Speech and dialogue systems