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A Survey on the Semi Supervised Learning Paradigm in the Context of Speech Emotion Recognition
- Source :
- Lecture Notes in Networks and Systems ISBN: 9783030821951, IntelliSys (2)
- Publication Year :
- 2021
- Publisher :
- Springer International Publishing, 2021.
-
Abstract
- The area of Automatic Speech Emotion Recognition has been a hot topic for researchers for quite some time now. The recent breakthroughs on technology in the field of Machine Learning open up doors for multiple approaches of many kinds. However, some concerns have been persistent throughout the years where we highlight the design and collection of data. Proper annotation of data can be quite expensive and sometimes not even viable, as specialists are often needed for such a complex task as emotion recognition. The evolution of the semi supervised learning paradigm tries to drag down the high dependency on labelled data, potentially facilitating the design of a proper pipeline of tasks, single or multi modal, towards the final objective of the recognition of the human emotional mental state. In this paper, a review of the current single modal (audio) Semi Supervised Learning state of art is explored as a possible solution to the bottlenecking issues mentioned, as a way of helping and guiding future researchers when getting to the planning phase of such task, where many positive aspects from each piece of work can be drawn and combined.
- Subjects :
- Dependency (UML)
Computer science
business.industry
Deep learning
Context (language use)
02 engineering and technology
Semi-supervised learning
Pipeline (software)
Field (computer science)
Task (project management)
Modal
Human–computer interaction
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Subjects
Details
- ISBN :
- 978-3-030-82195-1
- ISBNs :
- 9783030821951
- Database :
- OpenAIRE
- Journal :
- Lecture Notes in Networks and Systems ISBN: 9783030821951, IntelliSys (2)
- Accession number :
- edsair.doi...........a03733123f73d50edc6877245c4358d5