Application of Vector Quantization in Emotion Recognition from Human Speech

Khanna, Preeti; Kumar, Mukesh

doi:10.1007/978-3-642-19423-8_13

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…This paper explores how to combine the information from various sources (e.g. facial expression [24], speech [21,22,23] and others [20]) to achieve better recognition of emotional state using rule based approach.…”

Section: The Problem Domain For Multimodal Emotion Recognitionmentioning

confidence: 99%

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Rule Based System for Recognizing Emotions Using Multimodal Approach

Khanna¹,

Sasikumar²

2013

IJACSA

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Abstract-Emotion is assuming increasing importance in human computer interaction (HCI), in general, with the growing feeling that emotion is central to human communication and intelligence. Users expect not just functionality as a factor of usability, but experiences, matched to their expectations, emotional states, and interaction goals. Endowing computers with this kind of intelligence for HCI is a complex task. It becomes more complex with the fact that the interaction of humans with their environment (including other humans) is naturally multimodal. In reality, one uses a combination of modalities and they are not treated independently. In an attempt to render HCI more similar to human-human communication and enhance its naturalness, research on multiple modalities of human expressions has seen ongoing progress in the past few years. As compared to unimodal approaches, various problems arise in case of multimodal emotion recognition especially concerning fusion architecture of multimodal information. In this paper we will be proposing a rule based hybrid approach to combine the information from various sources for recognizing the target emotions. The results presented in this paper shows that it is feasible to recognize human affective states with a reasonable accuracy by combining the modalities together using rule based system.

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Section: The Problem Domain For Multimodal Emotion Recognitionmentioning

confidence: 99%

“…We will use facial expressions as the running example to illustrate these stages, etc. The framework remains same across all modalities [20,21,22].…”

Section: Framework For Emotion Recognitionmentioning

confidence: 99%