Traditional automatic speech recognition (ASR) models output lower-cased words without punctuation marks, which reduces readability and necessitates a subsequent text processing model to convert ASR transcripts into a proper format. Simultaneously, the development of end-to-end ASR models capable of predicting punctuation and capitalization presents several challenges, primarily due to limited data availability and shortcomings in the existing evaluation methods, such as inadequate assessment of punctuation prediction. In this paper, we introduce a LibriSpeech-PC benchmark designed to assess the punctuation and capitalization prediction capabilities of end-to-end ASR models. The benchmark includes a LibriSpeech-PC dataset with restored punctuation and capitalization, a novel evaluation metric called Punctuation Error Rate (PER) that focuses on punctuation marks, and initial baseline models. All code, data, and models are publicly available.

翻译：传统自动语音识别（ASR）模型输出的是不含标点符号的小写单词，这降低了可读性，并需要后续文本处理模型将ASR转录文本转换为适当格式。与此同时，能够预测标点和大小写的端到端ASR模型的开发面临若干挑战，主要由于数据可用性有限以及现有评估方法的缺陷，例如对标点预测的评估不足。本文提出LibriSpeech-PC基准，旨在评估端到端ASR模型的标点与大小写预测能力。该基准包含恢复标点与大小写的LibriSpeech-PC数据集、聚焦标点符号的新型评估指标——标点错误率（PER），以及初始基线模型。所有代码、数据和模型均已公开。