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  "fetched_at": "2026-10-20T04:14:48Z",
  "source_url": "https://www.kaggle.com/competitions/gender-voice/data",
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      "content": "To analyze gender by voice and speech, a training database was required. A database was built using thousands of samples of male and female voices, each labeled by their gender of male or female. Voice samples were collected from the following resources:\n\nThe Harvard-Haskins Database of Regularly-Timed Speech\nTelecommunications & Signal Processing Laboratory (TSP) Speech Database at McGill University\nVoxForge Speech Corpus\nFestvox CMU_ARCTIC Speech Database at Carnegie Mellon University\nEach voice sample is stored as a.WAV file, which is then pre-processed for acoustic analysis using the specan function from the WarbleR R package. Specan measures 22 acoustic parameters on acoustic signals for which the start and end times are provided.\n\nThe output from the pre-processed WAV files were saved into a CSV file, containing 3168 rows and 21 columns (20 columns for each feature and one label column for the classification of male or female). You can download the pre-processed dataset in CSV format, using the link above.In order to analyze gender by voice and speech, a training database was required. A database was built using thousands of samples of male and female voices, each labeled by their gender of male or female. Voice samples were collected from the following resources:\n\nThe Harvard-Haskins Database of Regularly-Timed Speech\nTelecommunications & Signal Processing Laboratory (TSP) Speech Database at McGill University\nVoxForge Speech Corpus\nFestvox CMU_ARCTIC Speech Database at Carnegie Mellon University\nEach voice sample is stored as a.WAV file, which is then pre-processed for acoustic analysis using the specan function from the WarbleR R package. Specan measures 22 acoustic parameters on acoustic signals for which the start and end times are provided.",
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      "content": "# **Introduction**\nDetermining a person’s gender as male or female, based upon a sample of their voice seems to initially be an easy task. Often, the human ear can easily detect the difference between a male or female voice within the first few spoken words. However, designing a computer program to do this turns out to be a bit trickier.\n\nThis article describes the design of a computer program to model acoustic analysis of voices and speech for determining gender. The model is constructed using 3,168 recorded samples of male and female voices, speech, and utterances. The samples are processed using acoustic analysis and then applied to an artificial intelligence/machine learning algorithm to learn gender-specific traits. The resulting program achieves 89% accuracy on the test set.\n\n# **Hosts**\nThis competition is being hosted by the Society for Data Science, Birla Institute of Technology Mesra.",
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      "name": "Evaluation",
      "content": "## **EVALUATION Metric**\nThe Scoring Metric for this competition is categorization accuracy.\n\n## **Submission**\nThe submission format is given in the data. There should be two columns in the CSV file titled 'Id' and 'label' as given in the sample submission file.",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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      "name": "Data Description",
      "content": "The following acoustic properties of each voice are measured:\n\nmeanfreq: mean frequency (in kHz)\nsd: standard deviation of frequency\nmedian: median frequency (in kHz)\nQ25: first quantile (in kHz)\nQ75: third quantile (in kHz)\nIQR: interquantile range (in kHz)\nskew: skewness (see note in specprop description)\nkurt: kurtosis (see note in specprop description)\nsp.ent: spectral entropy\nsfm: spectral flatness\nmode: mode frequency\ncentroid: frequency centroid (see specprop)\npeakf: peak frequency (frequency with highest energy)\nmeanfun: average of fundamental frequency measured across acoustic signal\nminfun: minimum fundamental frequency measured across acoustic signal\nmaxfun: maximum fundamental frequency measured across acoustic signal\nmeandom: average of dominant frequency measured across acoustic signal\nmindom: minimum of dominant frequency measured across acoustic signal\nmaxdom: maximum of dominant frequency measured across acoustic signal\ndfrange: range of dominant frequency measured across acoustic signal\nmodindx: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range",
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