Speech Emotion Recognition using Machine Learning

Speech Emotion Recognition (SER) is an audio-AI final-year project that detects the speaker's emotion — happy, angry, sad, neutral and more — directly from voice recordings. Raw audio is converted into Mel-Frequency Cepstral Coefficients (MFCCs), the standard acoustic features used in speech research, and a convolutional neural network trained on the RAVDESS dataset classifies the emotion with a confidence score. The trained model is wrapped in VoiceMood, a Streamlit web app where you can upload or record a voice clip and instantly see the predicted emotion, a confidence radar chart and an emotion timeline for longer recordings.

Unlike image-classification projects that every examiner has seen a hundred times, this is an audio-AI build with real signal-processing depth — MFCC extraction, spectro-temporal learning, and a head-to-head CNN vs LSTM vs SVM comparison, all documented with confusion matrices and per-class F1 scores for your viva.

This is a built-to-order software project: the trained model, full source code and complete documentation are prepared and delivered for your final-year review.

1. The audio clip is loaded, resampled to a uniform rate (22.05 kHz), trimmed of leading/trailing silence and normalized.
2. librosa extracts 40 MFCCs plus their delta derivatives, forming a fixed-length time-frequency feature matrix (216 frames for a 3-second clip).
3. The trained CNN learns spectro-temporal patterns from this matrix and outputs a softmax distribution over the 8 emotion classes.
4. VoiceMood renders the waveform, the top predicted emotion, a confidence radar across all classes, and — for longer clips — an emotion timeline.
5. The report section compares CNN vs LSTM vs SVM with accuracy, confusion matrices and per-class F1, giving you examiner-ready talking points.

• Upload or record voice clips (WAV/MP3) directly in the browser
• MFCC + delta feature extraction with librosa — the industry-standard acoustic pipeline
• CNN classifier trained on the RAVDESS benchmark dataset (1,440 clips, 8 emotions)
• LSTM variant and SVM baseline included for model comparison in the report
• Confidence radar/bar chart for every prediction
• Emotion timeline view for longer audio (emotion per segment)
• Batch prediction mode for demoing a folder of clips
• Interactive Streamlit web app — runs on any laptop, no GPU needed for inference
• Colab training notebook included for re-training and viva demonstrations

• Full source code (Python, well-commented, modular)
• Trained CNN model file (.h5) + training Colab notebook
• Project report PDF (synopsis, literature survey, methodology, results, conclusion)
• PPT presentation for final review
• Viva Q&A preparation document (MFCC, CNN/LSTM, RAVDESS questions answered)

FAQs

1. Why is RAVDESS used instead of my own recordings? RAVDESS is the recognized public benchmark for speech emotion research — examiners trust results measured on it, and the report includes the dataset citation. The app still lets you test on your own voice recordings.
2. Why MFCC features instead of feeding raw audio to the model? MFCCs compress speech into a compact representation that mimics human hearing — this is the standard approach in published SER papers, keeps training fast, and is exactly what viva examiners expect you to explain.
3. CNN vs LSTM — which one is better for this project? The CNN learns local spectro-temporal patterns and trains faster; the LSTM captures longer prosodic dynamics. The report compares both against an SVM baseline with confusion matrices, so you can defend the choice either way.
4. Can I record my voice live in the app? Yes — VoiceMood supports mic recording in the browser as well as file upload, which makes for a strong live demo in your review.
5. How is accuracy measured for my review? The report documents the evaluation procedure for measuring accuracy on the RAVDESS held-out split, with the confusion matrix — examiners respect an honest benchmarked number over an inflated claim.
6. How can I extend this project? Common extensions are real-time streaming prediction, multilingual emotion datasets (e.g. EMO-DB), and fusing speech with text sentiment for multimodal emotion detection. Ask us and we will scope it.