Semi-supervised Learning Techniques for Speech Emotion Recognition

Autor:	K. V. Ahammed Muneer, K. Remya, B. S. Shajee Mohan
Rok vydání:	2021
Předmět:	History Computer science Speech recognition Semi-supervised learning Emotion recognition Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 1921:012029
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1921/1/012029
Popis:	The core objective of this paper is to explore semi-supervised learning methods for recognizing emotions contained in speech. Semi-supervised learning techniques are used when the availability of labeled examples are sparse. There are different methods that are used in the semi-supervised settings. These techniques include generative model, graph based methods, metric based methods etc., The speech emotion data is considered for this experiment. Speech signal contains emotion specific data also. Emotion dependent features are extracted from the speech. This paper aim to enhance some existing techniques for semi-supervised learning that are used for speech emotion recognition.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e60acc45d2a03e03edd3f8bb8055c1db https://doi.org/10.1088/1742-6596/1921/1/012029 Zobrazit plný text záznamu