Automatic speech recognition (ASR) systems play a key role in applications
involving human-machine interactions. Despite their importance, ASR models for
the Portuguese language proposed in the last decade have limitations in
relation to the correct identification of punctuation marks in automatic
transcriptions, which hinder the use of transcriptions by other systems,
models, and even by humans. However, recently Whisper ASR was proposed by
OpenAI, a general-purpose speech recognition model that has generated great
expectations in dealing with such limitations. This chapter presents the first
study on the performance of Whisper for punctuation prediction in the
Portuguese language. We present an experimental evaluation considering both
theoretical aspects involving pausing points (comma) and complete ideas
(exclamation, question, and fullstop), as well as practical aspects involving
transcript-based topic modeling - an application dependent on punctuation marks
for promising performance. We analyzed experimental results from videos of
Museum of the Person, a virtual museum that aims to tell and preserve people's
life histories, thus discussing the pros and cons of Whisper in a real-world
scenario. Although our experiments indicate that Whisper achieves
state-of-the-art results, we conclude that some punctuation marks require
improvements, such as exclamation, semicolon and colon.

本文研究了 OpenAI 的 Whisper ASR 在葡萄牙语中标点预测的性能。通过对虚拟博物馆中视频的实验评估，结果表明 Whisper ASR 达到了最先进的水平，但感叹号、分号和冒号等标点的识别仍需要改进。