Spoken language understanding for social robotics
| Autor: | Ismael García-Varea, Cristina Romero-González, Jesus Martínez-Gómez |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Social robot
Computer science business.industry Semantic interpretation Natural language understanding 02 engineering and technology computer.software_genre Semantics Feature (linguistics) 030507 speech-language pathology & audiology 03 medical and health sciences 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Artificial intelligence 0305 other medical science business computer Natural language Natural language processing Spoken language |
| Zdroj: | ICARSC |
| DOI: | 10.1109/icarsc49921.2020.9096175 |
| Popis: | Speech understanding is a fundamental feature of social robots, since spoken language is the most natural mean of human-human communication. Providing a robot with the ability to understand human language makes it much more accessible to a wide range of users, especially for those who are not experts in the field. Speech understanding is composed of two sub-tasks. The first one is known as automatic speech recognition (ASR), which is the process of translating or transcribing an audio signal into a written text. The second one is natural language understanding (NLU), which consists in obtaining a semantic interpretation from the (previously) transcribed text. In this work, we present a speech-input natural language understanding system for social robots which has been successfully tested with the well-known HuRIC v1.2 corpus obtaining state-of-the art results. Preliminary versions of the proposed system were also tested in real scenarios during the last two editions of the RoCKIn@Home competition, where we were classified in first and second positions respectively. |
| Databáze: | OpenAIRE |
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