Voice Gender Recognition

In this project it was used some different Machine Learning models to identify the gender of a voice (Female or Male) based on some specific speech and voice attributes.

Models implemented by Anne Livia.

Dataset Information:

• This dataset was obtained from Kaggle on this link by Kory Becker and was created to identify a voice as male or female, based upon acoustic properties of the voice and speech.
• The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz (human vocal range).

Properties:

• meanfreq: mean frequency (in kHz)
• sd: standard deviation of frequency
• median: median frequency (in kHz)
• Q25: first quantile (in kHz)
• Q75: third quantile (in kHz)
• IQR: interquantile range (in kHz)
• skew: skewness (see note in specprop description)
• kurt: kurtosis (see note in specprop description)
• sp.ent: spectral entropy
• sfm: spectral flatness
• mode: mode frequency
• centroid: frequency centroid (see specprop)
• meanfun: average of fundamental frequency measured across acoustic signal
• minfun: minimum fundamental frequency measured across acoustic signal
• maxfun: maximum fundamental frequency measured across acoustic signal
• meandom: average of dominant frequency measured across acoustic signal
• mindom: minimum of dominant frequency measured across acoustic signal
• maxdom: maximum of dominant frequency measured across acoustic signal
• dfrange: range of dominant frequency measured across acoustic signal
• modindx: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental ---- **frequencies divided by the frequency range
• label: male or female

Software Informations

• Python
• Scikit-learn
• Matplotlib
• Seaborn

Trained Models

• Decision Tree Model

  • Acurracy: 0.9652996845425867

  • Precision: 0.9715189873417721

  • Recall: 0.959375

  • F1-Score: 0.9654088050314465

  • Confusion Matrix, Feature Importance, and Precision-Recall Curve respectively:

    

• Random Forest Model

  • Acurracy: 0.9810725552050473

  • Precision: 0.9842767295597484

  • Recall: 0.978125

  • F1-Score: 0.9811912225705329

  • Confusion Matrix, Feature Importance, and Precision-Recall Curve respectively:

    

• Extra Tree Model

  • Acurracy: 0.9873817034700315

  • Precision: 0.9905660377358491

  • Recall: 0.984375

  • F1-Score: 0.9874608150470221

  • Confusion Matrix, Feature Importance, and Precision-Recall Curve respectively:

    

• XGBoost model

  • Acurracy: 0.9873817034700315

  • Precision: 0.9905660377358491

  • Recall: 0.984375

  • F1-Score: 0.9874608150470221

  • Confusion Matrix, Feature Importance, and Precision-Recall Curve respectively: