Emotion Recognition from Video Audio and Text
Abstract
Emotion detection from video, audio, and text has
become a key focus in artificial intelligence and
human-computer
interaction.
As
digital
communication increasingly involves multiple
modalities, accurately interpreting human emotions
is essential for enhancing user experience,
supporting mental health diagnostics, and
advancing affective computing. This paper provides
a comprehensive overview of emotion recognition
methods across video, audio, and text, exploring the
unique contributions of each modality and their
combined potential in multimodal systems. The analysis begins by examining how each
modality contributes to emotion detection. Video
leverages facial expressions, gestures, and body
language using computer vision, while audio
focuses on vocal traits such as pitch and tone
through signal processing and machine learning.
Text-based emotion detection uses natural language
processing to interpret sentiment and context from
written content. When integrated, these modalities
create more accurate and resilient emotion
recognition systems that mirror the complexity of
human emotion.
The paper also explores key challenges, including
data
synchronization,
multimodal
feature
monitoring, educational technology, and many other
domains.
Human emotions are expressed through multiple
channels — facial expressions, speech patterns, and
the words people use. Video captures facial
movements and body language; audio captures voice
tone, pitch, and rhythm; text conveys the semantic extraction, and the scarcity of diverse, annotated
datasets. Advances in machine learning, especially
deep learning techniques like transformers and
attention mechanisms, have significantly improved
emotion
detection
performance.
Potential
applications include mental health monitoring,
customer service, education, and interactive
entertainment. The paper concludes by highlighting
future
research
needs,
including
ethical
considerations, generalizable models, and real-time
emotion recognition, aiming to create AI systems
that better understand and respond to human
emotions.
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