This paper explains our work in developing new acoustic models for automated
speech recognition (ASR) at KBLab, the infrastructure for data-driven research
at the National Library of Sweden (KB). We evaluate different approaches for a
viable speech-to-text pipeline for audiovisual resources in Swedish, using the
wav2vec 2.0 architecture in combination with speech corpuses created from KB's
collections. These approaches include pretraining an acoustic model for Swedish
from the ground up, and fine-tuning existing monolingual and multilingual
models. The collections-based corpuses we use have been sampled from millions
of hours of speech, with a conscious attempt to balance regional dialects to
produce a more representative, and thus more democratic, model. The acoustic
model this enabled, "VoxRex", outperforms existing models for Swedish ASR. We
also evaluate combining this model with various pretrained language models,
which further enhanced performance. We conclude by highlighting the potential
of such technology for cultural heritage institutions with vast collections of
previously unlabelled audiovisual data. Our models are released for further
exploration and research here: this https URL.

本文通过使用 wav2vec 2.0 结构与来自瑞典国家图书馆 (KB) 收藏的语音语料库，对实现适用于瑞典语音频资源的语音文本管道的不同方法进行了评估并进行了改进。最终，作者提出的 VoxRex 声学模型表现出比现有瑞典语言 ASR 模型更好的性能，并强调了这样的技术在文化遗产机构中处理大量未标记音频视觉数据的潜力。