stuttering-detection

GitHub

https://github.com/Earwigmoth10/stuttering-detection-classifier.git

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title: SpeakFlow AI emoji: colorFrom: blue colorTo: teal sdk: docker app_port: 5000 pinned: false

SpeakFlow AI

AI-Powered Speech Stuttering Detection System

SpeakFlow AI analyzes uploaded or recorded speech audio and detects whether it contains normal speech or stuttering patterns, using MFCC feature extraction and a Random Forest classifier.

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Features

• Audio upload & live in-browser recording
• ML-based stutter detection (40 MFCC features + Random Forest)
• Confidence & fluency scoring on every prediction
• Downloadable PDF report of each analysis
• Searchable analysis history
• User accounts (sign up, log in, edit profile, change password)
• Admin dashboard — user management, analytics charts, CSV export
• Dark mode, responsive UI

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Frontend

Tech Stack

Frontend: HTML5, CSS3, Vanilla JavaScript, Chart.js, jsPDF, Font Awesome Backend: Python, Flask, Flask-CORS, Librosa, Scikit-learn, NumPy, Joblib

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How It Works

1. User uploads or records a WAV/MP3 file
2. Audio is preprocessed (noise reduction, normalization, silence trimming)
3. 40 MFCCs are extracted using Librosa
4. Feature vector is classified by a trained Random Forest model
5. App returns Normal / Stutter prediction with a confidence score

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Dataset

This model was trained using publicly available speech datasets from the following sources:

Stuttering Speech Dataset

• UCLASS Stuttered Speech Clips (SEP-28k Format)
• Source: https://www.kaggle.com/datasets/vudominhgiang/uclass-stuttered-speech-clips-sep-28k-format

Fluent (Normal) Speech Dataset

• Mozilla Common Voice
• Source: https://commonvoice.mozilla.org/

The datasets were preprocessed and combined to create a binary classification dataset for distinguishing between Fluent Speech and Stuttering Speech.

Note: The datasets are not redistributed in this repository. Please download them from their respective official sources and ensure compliance with their licenses before use.

Running This Space

This Space runs a Flask backend that serves the ML prediction API. On startup it will be available at the URL shown in the Space's embedded app window.

• Backend endpoint: /api/predict (POST, multipart audio upload)
• Health check: /

Note: This app was originally built to run with a separate local frontend (index.html + http.server) talking to 127.0.0.1:5000. When deployed here, the frontend's API base URL points at this Space's backend instead of localhost.

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Admin Access

An admin account is configured in the login flow to access the admin dashboard (user management, analytics, CSV export).

Demo project notice: User accounts, sessions, and analysis history are stored in browser localStorage, not a real database, and passwords are not hashed. This project is for academic/demo purposes only — not intended for production use with real user data.

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Model Details

• Feature extraction: 40 MFCCs averaged over time (Librosa)
• Classifier: Random Forest (Scikit-learn)
• Classes: Normal speech vs. Stuttered speech
• Training data: 8,000+ labeled audio samples

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Author

Laiba Aamir (reawigmoth)