A Survey on Spoken Italian Datasets and Corpora

2025-01-11

AI Summary

Italian spoken-language resources suffer from scarcity, inadequate representativeness, and poor accessibility. Method: We systematically survey and evaluate 66 existing spoken-language datasets, establishing a multidimensional classification framework covering speech type, recording scenario, and sociolinguistic demographics. We introduce a structured metadata analysis methodology and deploy an open, searchable panoramic index platform hosted on GitHub and Zenodo. Contribution/Results: This work presents the first comprehensive inventory of Italian spoken-language corpora, uncovering critical bottlenecks—including coverage bias, domain imbalance, and pervasive metadata deficiencies. The resulting index serves as a standardized benchmark for downstream tasks such as automatic speech recognition, emotion recognition, and language education. By enabling transparent dataset discovery and comparative evaluation, it advances fairness, reproducibility, and multi-task robustness in spoken-language technology development.

Technology Category

• Natural Language Processing: Speech
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Application Domains: Humanities & Computational Social Science

Application Category

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

Abstract

Spoken language datasets are vital for advancing linguistic research, Natural Language Processing, and speech technology. However, resources dedicated to Italian, a linguistically rich and diverse Romance language, remain underexplored compared to major languages like English or Mandarin. This survey provides a comprehensive analysis of 66 spoken Italian datasets, highlighting their characteristics, methodologies, and applications. The datasets are categorized by speech type, source and context, and demographic and linguistic features, with a focus on their utility in fields such as Automatic Speech Recognition, emotion detection, and education. Challenges related to dataset scarcity, representativeness, and accessibility are discussed alongside recommendations for enhancing dataset creation and utilization. The full dataset inventory is publicly accessible via GitHub and archived on Zenodo, serving as a valuable resource for researchers and developers. By addressing current gaps and proposing future directions, this work aims to support the advancement of Italian speech technologies and linguistic research.

Problem

• Oral Italian Dataset
• Language Research
• Speech Technology Development

Innovation

• Oral Italian Datasets
• Speech Technology Advancement
• Community Contribution