Understanding Emotional Tone using NLP and ML Algorithms

Abstract

Emotional intelligence is pivotal in improving the interaction between humans and machines, especially for building AI systems that are user-friendly. This research proposes a new framework that enables interactive, emotion-sensitive communication between users and digital systems. It achieves this by integrating speech emotion detection with the generation of empathetic responses. Emotions like happiness, sadness, and anger are recognized using both acoustic signals (e.g., MFCC features and Librosa) and textual inputs. A deep learning setup utilizing CNN and LSTM networks ensures precise emotion recognition. Furthermore, the system uses advanced NLP models such as BERT and GPT to either generate or retrieve motivational quotes aligned with the detected emotional state. These responses are vocalized using text-to-speech tools to support natural verbal exchanges. The final implementation is an interactive web app that delivers emotionally intelligent assistance in real time. Test results indicate that the system performs effectively in recognizing emotions and delivering contextually appropriate feedback, showcasing its utility in domains like mental wellness and AI-driven personalization.

Introduction

With technology increasingly integrated into daily life, recognizing and responding to human emotions has become essential for human-computer interaction (HCI). This study introduces a web-based application capable of detecting emotions from speech in real time and responding with voice-generated, emotionally aware feedback. It combines speech processing, deep learning, and natural language processing (NLP) to create personalized, empathetic user experiences. Applications include digital assistants, emotional wellness support, and intelligent educational tools.

Key Components of the System:

1. Literature Survey

• Emotion recognition from speech uses features like MFCCs and spectral patterns, extracted using tools like Librosa.

• CNNs and LSTMs are popular deep learning models used for classifying emotions.

2. Existing Systems

• Typically rely on DNNs and datasets like the Berlin Emotional Speech Database.

• Recognize limited emotions (e.g., anger, sadness, neutrality) after preprocessing steps like silence removal.

3. Proposed System

• Combines acoustic and textual analysis for more accurate emotion recognition.

• Uses transformer models (BERT, GPT) for natural language understanding.

• Integrates a web interface and text-to-speech (TTS) for interactive responses.

• Also includes motivational quote recommendations based on detected emotions.

4. Implementation Process

• Captures voice input, converts it to text, and analyzes both audio and text for emotional content.

• Uses NLP and transformer-based models to generate empathetic responses.

• Delivers responses through TTS for human-like interaction.

Modules:

1. Speech Input – Captures and transcribes voice.

2. Emotion Recognition – Detects emotional state using audio + text.

3. Response Generation – Produces context-aware replies using NLP.

4. Voice Response – Converts replies into speech.

5. Quote Recommendation – Suggests motivational quotes tailored to the emotion.

Algorithms Used:

• Speech Recognition: PyAudio, Google Speech-to-Text, Whisper.

• Audio Feature Extraction: MFCCs, chroma, spectrograms via Librosa.

• Emotion Classification: CNN, LSTM, SVM, Random Forest.

• Response Generation: GPT-2, GPT-3, BART, T5 with Hugging Face models.

• TTS Engines: pyttsx3, Google TTS, Microsoft Edge TTS.

Results:

The system successfully detects various emotions (e.g., neutral, fear, disgust) and responds appropriately with voice feedback and motivational quotes, enhancing user interaction and emotional support.

Conclusion

This emotion-aware framework facilitates more natural and empathetic communication by leveraging natural language processing and machine learning to recognize and interpret emotional cues. Through real-time, voice-driven interaction and the delivery of motivational responses, the system significantly improves user engagement. This initiative marks a notable advancement in building emotionally intelligent AI systems with broad potential across sectors like mental wellness support and virtual assistance.

References

[1] Ahmed, M. R., Islam, S., & Islam, A. K. M. M. (n.d.). Improved Speech Emotion Recognition Using an Ensemble of CNN-LSTM-GRU Models with Data Augmentation. [2] Hamsa, S., Shahin, I., & Iraqi, Y. (2020). Speech Emotion Classification Using Wavelet Packet Transform and Random Forest. IEEE Access, 8, 96994–97006. [3] Murugan, H. (n.d.). Exploring Speech Emotion Recognition Using Convolutional Neural Networks (CNNs). SRM Institute of Science and Technology.

Copyright

Copyright © 2025 Mrs. M. Anitha, G. Pavani, V. Gunasai, Sd. Jaibunnisa, A. Ananya. 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.