Transformer-based Speech Recognition Models for Oral History Archives in English, German, and Czech

Abstract

This paper is a step forward in our effort to make vast oral history archives more accessible to the public and researchers by breaking down the decoding barriers between the knowledge encoded in the spoken testimonies and users who want to search for the information of their interest. We present new Transformer-based monolingual models suitable for speech recognition of oral history archives in English, German, and Czech. Our experiments show that although the all-purpose speech recognition systems have recently made tremendous progress, the transcription of oral history archives is still a challenging task for them; our tailored models significantly outperformed larger public multilingual models and scored new state-of-the-art results on all tested datasets. Due to the 2-phase fine-tuning process, our models are robust and can be used for oral history archives of various domains. We publicly release our models within a public speech recognition service.