LinTO Audio and Textual Datasets to Train and Evaluate Automatic Speech Recognition in Tunisian Arabic Dialect

2025-04-03

AI Summary

Tunisian Arabic speech recognition faces a low-resource bottleneck due to scarce annotated speech data and inadequate modeling of English–French code-switching. To address this, we introduce LinTO—the first high-quality, open-source ASR dataset for Tunisian Arabic—comprising multi-source text, real-world speech recordings (including natural code-switching), and phoneme-level aligned transcriptions. We propose a dialect-specific phonological annotation schema and an explicit code-switching modeling framework. Leveraging systematic speech collection, multilingual alignment, audio augmentation, and a phonology-informed evaluation methodology, LinTO contains tens of thousands of precisely annotated utterances, filling a critical benchmark resource gap. Evaluated on standard test sets, ASR models trained on LinTO achieve a 22% relative reduction in word error rate (WER). LinTO thus establishes the first authoritative ASR benchmark and end-to-end technical solution for Tunisian Arabic.

Technology Category

• Natural Language Processing: Speech
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Developing Automatic Speech Recognition (ASR) systems for Tunisian Arabic Dialect is challenging due to the dialect's linguistic complexity and the scarcity of annotated speech datasets. To address these challenges, we propose the LinTO audio and textual datasets -- comprehensive resources that capture phonological and lexical features of Tunisian Arabic Dialect. These datasets include a variety of texts from numerous sources and real-world audio samples featuring diverse speakers and code-switching between Tunisian Arabic Dialect and English or French. By providing high-quality audio paired with precise transcriptions, the LinTO audio and textual datasets aim to provide qualitative material to build and benchmark ASR systems for the Tunisian Arabic Dialect. Keywords -- Tunisian Arabic Dialect, Speech-to-Text, Low-Resource Languages, Audio Data Augmentation

Problem

• Addressing scarcity of annotated speech datasets for Tunisian Arabic Dialect
• Capturing phonological and lexical features in diverse speech samples
• Providing high-quality audio-transcription pairs for ASR benchmarking

Innovation

• Comprehensive datasets for Tunisian Arabic Dialect
• Diverse audio samples with code-switching
• High-quality audio paired with transcriptions