Is Semi-Automatic Transcription Useful in Corpus Creation? Preliminary Considerations on the KIParla Corpus

2026-03-17

AI Summary

This study investigates the trade-off between efficiency and accuracy in semi-automatic transcription for spoken language corpus construction. Through a two-stage experiment, it compares the performance of expert and novice transcribers on three types of Italian conversational data under both manual and ASR-assisted conditions. The work proposes an integrated analytical framework combining word-level alignment, quality evaluation metrics, and statistical modeling to systematically quantify behavioral differences across transcription workflows. Results demonstrate that ASR substantially increases transcription speed, yet its impact on accuracy varies depending on dialogue type, transcriber expertise, and workflow configuration. The findings provide empirical support for the development of the KIParla corpus, showing that a fine-tuned and optimized semi-automatic pipeline can effectively accelerate annotation while maintaining high transcription quality.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Semi-Supervised Learning

Application Category

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

Abstract

This paper analyses the implementation of Automatic Speech Recognition (ASR) into the transcription workflow of the KIParla corpus, a resource of spoken Italian. Through a two-phase experiment, 11 expert and novice transcribers produced both manual and ASR-assisted transcriptions of identical audio segments across three different types of conversation, which were subsequently analyzed through a combination of statistical modeling, word-level alignment and a series of annotation-based metrics. Results show that ASR-assisted workflows can increase transcription speed but do not consistently improve overall accuracy, with effects depending on multiple factors such as workflow configuration, conversation type and annotator experience. Analyses combining alignment-based metrics, descriptive statistics and statistical modeling provide a systematic framework to monitor transcription behavior across annotators and workflows. Despite limitations, ASR-assisted transcription, potentially supported by task-specific fine-tuning, could be integrated into the KIParla transcription workflow to accelerate corpus creation without compromising transcription quality.

Problem

• Automatic Speech Recognition
• corpus creation
• transcription workflow
• spoken Italian
• ASR-assisted transcription

Innovation

• Automatic Speech Recognition
• corpus creation
• transcription workflow
• word-level alignment
• statistical modeling