Speech-to-text Recognition for the Creation of Subtitles in Basque

Abstract

This contribution aims at analysing the speech-to-text recognition of news programmes in the regional channel ETB1 for subtitling in Basque using ADITU (2024) (a technology developed by the Elhuyar foundation) applying the NER model of analysis (Romero-Fresco and Martínez 2015). A total of 20 samples of approximately 5 minutes each were recorded from the regional channel ETB1 in May, 2022. A total of 97 minutes and 1737 subtitles were analysed by applying criteria from the NER model. The results show an average accuracy rate of 94.63% if we take all errors into account, and 96.09% if we exclude punctuation errors. A qualitative analysis based on quantitative data foresees some room for improvement regarding language models of the software, punctuation, recognition of proper nouns and speaker identification. From the evidence it may be concluded that, although quantitative data does not reach the threshold to consider the quality of recognition fair or comprehensible with regards to the NER model, results seem promising. When presenters speak with clear diction and standard language, accuracy rates are sufficient for a minority language like Basque in which speech recognition software is still in early phases of development.

Keywords

• NER model
• speech-to-text
• speech recognition
• Basque
• accessibility
• intralingual subtitling