Annotation Tool: anotações de dados por meio de áudios.

Abstract

This course completion work addresses the development of a web tool for the data annotation project at the Computer Laboratory Intelligent Applied(LACINA). This tool focuses on dialogue-based audio annotations, with the aim of facilitating the creation of datasets obtained for training dialogue systems based on machine learning. The tool was designed to provide an efficient and intuitive platform, allowing the identification of speakers and the marking of intentions and entities in audio dialogues. The project highlights the importance of dialog-based audio annotations to the advancement of dialog systems, and discusses the advantages of having annotated datasets, as well as the ethical and privacy considerations associated with this process. The development session addresses the technical aspects of the tool, including the choice of technologies and frameworks used, the challenges faced during the course and the solutions adopted to overcome them. Desirable functionalities were highlighted, such as a friendly user interface, advanced playback features and synchronized visualization of audio and annotated text. Then, practical applications of the tool in the context of machine learning and natural language processing projects were discussed. The annotated datasets created with the tool can be used to train speech recognition and language comprehension models, contributing to the development of conversational dialogue systems. This work aims to provide a comprehensive overview of this ever-evolving area, highlighting its impact and its potential to drive research and development of audio-based machine learning applications. The dialog-based audio annotations web tool represents a significant contribution to the field of natural language processing and dialog systems, facilitating the creation of high quality annotated datasets and driving the advancement and development of more advanced dialog systems. efficient, natural and accurate.