Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM

Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM

Emotional state recognition of a speaker is a difficult task for machine learning algorithms which plays an important role in the field of speech emotion recognition (SER). SER plays a significant role in many real-time applications such as human behavior assessment, human-robot interaction, virtual...

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Bibliographic Details
Main Authors: Mustaqeem, Muhammad Sajjad, Soonil Kwon
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Speech emotion recognition
deep bidirectional long shot term memory
key segment sequence selection
normalization of CNN features
radial-based function network (RBFN)
Online Access:https://ieeexplore.ieee.org/document/9078789/

Internet

https://ieeexplore.ieee.org/document/9078789/

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