Emotion Recognition from Text and Voice: A Multimodal AI Approach to Understanding Human Feelings

Abstract

Now, in our global online world, technology acts as an interface for communications within our lives, from intelligent assistants to internet customer support and even social networking. Humans, being proficient at interpreting the feelings that come with communication, are now done by machines. The coating of emotions conveying words in real time continues to be an enigma still unsolved. This project tries to answer the question of how to solve machines interpreting emotions. Presenting a multimodal AI system that interprets emotions based on the simultaneous examination of speech and text content with NLP and speech analysis algorithms and a fusion-based deep learning system is what our work revolves around. Using cutting-edge NLP and speech processing, we are creating systems that decode for content, as well as conduct and ethics. The backbone of the system for voice content decoding is the emotional cues of the language processing, or L through. Our model utilizes the transformer models Distil BERT and Roberta. Emotions are also present in the voice with MFCCs and speech as a series of frames via chroma spectrograms, which are processed by CNN-LSTM hybrids for emotion recognition from voice. More sophisticated models are also built for further fusion. Our performance on model as well as on individual streams enhances emotion detection systems. We accomplish in this project combining various models. The voice and the text are processed independently and different outputs are provided by each model.

Introduction

Emotions play a vital role in human communication, often more than the words themselves. As AI and digital communication tools become integral to our lives, it is essential that these systems evolve beyond understanding just what we say to grasping how we feel. The proposed research introduces a multimodal emotion recognition system that combines textual sentiment analysis with voice-based emotion detection to improve emotional intelligence in machines.

Problem Statement

• Text-only systems miss emotional nuance such as sarcasm or irony.

• Voice-only systems lack context and can misinterpret high pitch or volume.

• Monomodal systems (text or voice alone) are inadequate; a hybrid approach is necessary.

• Misreading emotions in real applications (e.g., therapy, education, customer service) can have serious consequences.

• A combined model can produce more accurate and human-like emotional understanding.

Objectives

1. Detect emotions using both written text and spoken voice.

2. Build a hybrid AI model using NLP for text and acoustic features for voice.

3. Improve contextual accuracy, especially in ambiguous or complex emotional expressions.

4. Enable real-time emotion detection for interactive systems like chatbots, tutors, and therapy assistants.

Methodology

• Text Analysis: Preprocessing text with NLP techniques (e.g., tokenization, TF-IDF, BERT, RoBERTa).

• Voice Analysis: Extracting features like MFCC, Chroma, and spectrograms; using CNN-LSTM for emotion prediction.

• Fusion Model: Combining predictions from both text and voice (late fusion) to increase reliability using ensemble methods.

Results & Evaluation

• Text-only model: ~85% accuracy.

• Voice-only model: ~80% accuracy.

• Multimodal model: ~90–92% accuracy.

• Evaluation metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.

Conclusion: The multimodal system outperforms single-modality models, achieving a more holistic emotional understanding.

Applications

1. Mental Health Support: Detect early emotional distress via tone and language.

2. Emotion-Aware Learning: Adjust learning pace and support based on student emotions.

3. Social Media Monitoring: Detect public sentiment, hate speech, or crises in real-time.

4. Empathetic Virtual Assistants: Offer emotionally responsive interactions.

5. Customer Support: Detect and de-escalate frustration or anger.

6. Caregiving Robots: Identify loneliness or distress in elderly or children.

7. Gaming: Adapt difficulty and storyline based on player emotion.

8. Workplace Wellbeing: Monitor employee stress or disengagement through communication patterns.

9. Emergency Response: Prioritize support based on detected emotional urgency in calls or chats.

Future Enhancements

1. Facial Expression Recognition: Add visual emotion cues for a tri-modal system (text + voice + face).

2. Cultural & Multilingual Adaptation: Train models with culturally diverse and multilingual data to reduce bias.

3. Temporal Emotion Tracking: Understand emotional progression throughout a conversation.

4. Mobile Optimization: Build lightweight, offline-capable models for mobile and low-resource environments.

Literature Insights

Research supports the effectiveness of combining text and audio features, but also shows the limitations of current models—particularly in real-world, cross-cultural, or emotionally ambiguous contexts. The need for context-aware, ethical, and scalable solutions is emphasized, especially in fields like healthcare, education, and social media analysis.

Final Thoughts

Emotionally intelligent AI is no longer a futuristic vision—it is essential for improving human-computer interaction. This multimodal system aims to bridge the gap by enabling machines to understand not just words, but the emotions behind them, leading to more empathetic, accurate, and meaningful AI applications.

Conclusion

Human interaction is far more complex than the words we speak or type — it\'s a dense blend of tone, silence, facial expression, and implicit emotional cues. This research set out to bridge the chasm between the kinds of things machines can sense and the kinds of things humans actually experience. Combining Natural Language Processing (NLP) for text analysis with acoustic properties of speech, we created a multimodal system that could identify and read emotions with greater accuracy and subtlety. The most surprising thing about our results is this pattern: when both voice and text are analyzed, the system is more reliable at recognizing emotions than when analysed alone. This is because certain emotions might be hidden within word choice, and other emotions only with pitch, rate, or volume. Blending these perspectives provides the system with a richer emotional context so that it may respond in a way that sounds concerned and sensitive. The implications of this work extend beyond the laboratory. In psychiatric care, such a system could identify emotional distress early on and deliver interventions early enough. In education, it could adjust classrooms to accommodate the emotional needs of the students. In customer support, it could de-escalate frustration before it reaches anger. Most importantly, it allows for the introduction of AI systems that interact not as cool data processors, but as acquaintances that are attuned to — and appropriately respond to — the human experience. While results are promising, this is just the beginning. Emotions have many dimensions, are complex and subject to individual, social, and cultural variables. Future steps might include visual features like faces, extend to multilingual contexts, and track changes across time in emotions to build truly adaptive, empathetic systems. Fundamentally, this book is a step toward giving machines not merely the ability to \"hear\" and \"read\" us, but to listen — to the heart behind the words — and respond in such a way as is natural, human, and responsive to the feelings.

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Copyright

Copyright © 2025 Mansara Sharma, Dr. Umadevi Ramamoorthy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.