The sport has been a part of our lives since the beginning of times, whether we are spectators or participants. The diffusion and increase of multimedia platforms made the consumption of these contents available to everyone. Sports videos appeal to a large population all around the world and have become an important form of multimedia content that is streamed over the Internet and television networks. Moreover, sport content creators want to provide the users with relevant information such as live commentary, summarization of the games in form of text or video using automatic tools.As a result, MOG-Technologies wants to create a tool capable of summarizing football matches based on semantic content, and this problem was explored in the scope of this Dissertation. The main objective is to convert the television football commentator's speech into text taking advantage of Google's Speech-to-Text tool. Several machine learning models were then tested to classify sentences into important events. For the model training, a dataset was created, combining 43 games transcription from different television channels also from 72 games provided by Google Search timeline commentary, the combined dataset contains 3260 sentences. To validate the proposed solution the accuracy and f1 score were extracted for each machine learning model.The results show that the developed tool is capable of predicting events in live events, with low error rate. Also, combining multiple sources, not only the sport commentator speech, will help to increase the performance of the tool. It is important to notice that the dataset created during this Dissertation will allow MOG-Technologies to expand and perfect the concept discussed in this project.
| Identifer | oai:union.ndltd.org:up.pt/oai:repositorio-aberto.up.pt:10216/122451 |
| Date | 28 September 2019 |
| Creators | Miguel André Almeida Tomás Ferreira de Barros |
| Contributors | Faculdade de Engenharia |
| Source Sets | Universidade do Porto |
| Language | English |
| Detected Language | English |
| Type | Dissertação |
| Format | application/pdf |
| Rights | openAccess |
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