Upload your voice and receive intelligent speech fluency analysis using Machine Learning — powered by MFCC feature extraction and Random Forest classification.
A complete ML pipeline from raw audio to intelligent prediction.
8,000+ labeled audio samples of normal and stuttered speech collected from diverse speakers to build a robust training dataset.
Background noise removal, normalization, and silence trimming ensure clean signals before feature extraction begins.
Mel-Frequency Cepstral Coefficients (40 features) are extracted using Librosa — the gold standard for speech analysis.
An ensemble of decision trees classifies speech patterns, providing both prediction and probability estimates for each class.
The final output delivers binary classification (Normal / Stutter) along with a confidence score and fluency rating.
Upload a WAV or MP3 file to run the stuttering detection model.
Supports WAV and MP3 formats
Use your microphone to record speech and analyze it directly.
Your speech fluency analysis results appear here after processing.
No analysis run yet. Upload or record audio above to get results.
All previous analyses stored locally on your device.
| # | Date & Time | File Name | Result | Confidence | Action |
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No history yet. Run an analysis to see results here.
A step-by-step walkthrough of the machine learning pipeline.
User uploads a WAV or MP3 audio file. The file is validated for format, size, and duration before being passed to the preprocessing pipeline.
The audio signal is cleaned — background noise is reduced using spectral subtraction, amplitude is normalized, and silences are trimmed to isolate speech.
Librosa extracts 40 Mel-Frequency Cepstral Coefficients from the processed signal. These features encode the tonal characteristics of speech, capturing rhythm disruptions indicative of stuttering.
The 40-feature vector is fed into a trained Random Forest ensemble. Each tree votes on the classification; the majority determines the predicted class and probability score.
The final prediction — Normal or Stuttering — is returned with a confidence score, fluency rating, and processing time. The result is stored in history and available as a PDF report.