Emotion Recognition from Speech for an Interactive Robot Agent

Anjum, Madiha

doi:10.1109/sii.2019.8700376

Cited by 8 publications

(1 citation statement)

References 14 publications

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“…Human-Computer-Interaction (HCI) aims to enhance the sophisticated usage of any system. If a system can to understand human emotions, the response could be much better [1]. Emotion Recognition has many practical applications.…”

Section: Introductionmentioning

confidence: 99%

Design of Hierarchical Classifier to Improve Speech Emotion Recognition

Vasuki¹

2023

Computer Systems Science and Engineering

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Automatic Speech Emotion Recognition (SER) is used to recognize emotion from speech automatically. Speech Emotion recognition is working well in a laboratory environment but real-time emotion recognition has been influenced by the variations in gender, age, the cultural and acoustical background of the speaker. The acoustical resemblance between emotional expressions further increases the complexity of recognition. Many recent research works are concentrated to address these effects individually. Instead of addressing every influencing attribute individually, we would like to design a system, which reduces the effect that arises on any factor. We propose a two-level Hierarchical classifier named Interpreter of responses (IR). The first level of IR has been realized using Support Vector Machine (SVM) and Gaussian Mixer Model (GMM) classifiers. In the second level of IR, a discriminative SVM classifier has been trained and tested with meta information of first-level classifiers along with the input acoustical feature vector which is used in primary classifiers. To train the system with a corpus of versatile nature, an integrated emotion corpus has been composed using emotion samples of 5 speech corpora, namely; EMO-DB, IITKGP-SESC, SAVEE Corpus, Spanish emotion corpus, CMU's Woogle corpus. The hierarchical classifier has been trained and tested using MFCC and Low-Level Descriptors (LLD). The empirical analysis shows that the proposed classifier outperforms the traditional classifiers. The proposed ensemble design is very generic and can be adapted even when the number and nature of features change. The first-level classifiers GMM or SVM may be replaced with any other learning algorithm.

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Section: Introductionmentioning

confidence: 99%

Design of Hierarchical Classifier to Improve Speech Emotion Recognition

Vasuki¹

2023

Computer Systems Science and Engineering

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NOSE Algorithm for Offline English Speech Interaction Recognition System

Pan

2022

Cyber Security Intelligence and Analytics

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Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research

Stock‐Homburg

2021

Int J of Soc Robotics

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Knowledge production within the interdisciplinary field of human–robot interaction (HRI) with social robots has accelerated, despite the continued fragmentation of the research domain. Together, these features make it hard to remain at the forefront of research or assess the collective evidence pertaining to specific areas, such as the role of emotions in HRI. This systematic review of state-of-the-art research into humans’ recognition and responses to artificial emotions of social robots during HRI encompasses the years 2000–2020. In accordance with a stimulus–organism–response framework, the review advances robotic psychology by revealing current knowledge about (1) the generation of artificial robotic emotions (stimulus), (2) human recognition of robotic artificial emotions (organism), and (3) human responses to robotic emotions (response), as well as (4) other contingencies that affect emotions as moderators.

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Emotion Recognition from Speech for an Interactive Robot Agent

Cited by 8 publications

References 14 publications

Design of Hierarchical Classifier to Improve Speech Emotion Recognition

Design of Hierarchical Classifier to Improve Speech Emotion Recognition

NOSE Algorithm for Offline English Speech Interaction Recognition System

Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research

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