Punctuated text prediction is crucial for automatic speech recognition as it enhances readability and impacts downstream natural language processing tasks. In streaming scenarios, the ability to predict punctuation in real-time is particularly desirable but presents a difficult technical challenge. In this work, we propose a method for predicting punctuated text from input speech using a chunk-based Transformer encoder trained with Connectionist Temporal Classification (CTC) loss. The acoustic model trained with long sequences by concatenating the input and target sequences can learn punctuation marks attached to the end of sentences more effectively. Additionally, by combining CTC losses on the chunks and utterances, we achieved both the improved F1 score of punctuation prediction and Word Error Rate (WER).

翻译：带标点文本的预测对于自动语音识别至关重要，因为它能提升可读性并影响下游自然语言处理任务。在流式场景中，实时预测标点的能力尤为理想，但这是一项极具挑战的技术难题。在本工作中，我们提出了一种方法，通过使用基于分块的Transformer编码器并采用连接主义时序分类损失进行训练，从输入语音中预测带标点文本。通过拼接输入序列和目标序列训练长序列，声学模型能够更有效地学习附在句末的标点符号。此外，通过结合分块与语句上的连接主义时序分类损失，我们实现了标点预测F1分数与词错误率的双重提升。