论文标题
国家图书馆的声音 - 瑞典语的语料库和声学模型
Hearing voices at the National Library -- a speech corpus and acoustic model for the Swedish language
论文作者
论文摘要
本文解释了我们在KBLAB开发新的声音识别的新声学模型（ASR）方面的工作，KBLAB是瑞典国家图书馆（KB）的数据驱动研究基础架构。我们使用WAV2VEC 2.0体系结构与KB的集合创建的语音库来评估了瑞典语中可行的语音到文本管道的不同方法。这些方法包括从头开始预处理瑞典的声学模型，以及对现有的单语和多语言模型进行微调。我们使用的基于收藏的库存是从数百万个小时的言论中取样的，并有意识地试图平衡区域方言，以产生更具代表性的模型，从而产生更为民主的模型。声音模型启用了“ Voxrex”，优于瑞典ASR的现有模型。我们还评估了将该模型与各种预处理的语言模型相结合，从而进一步提高了性能。最后，我们强调了这种技术对文化遗产机构的潜力，并拥有大量以前未标记的视听数据。我们的模型在此处发布以进行进一步探索和研究：https：//huggingface.co/kblab。
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: https://huggingface.co/KBLab.
