This project forked from aravindnatarajan/speakerrecognition
Deep neural net to identify speakers from a sample of their speech: LibriSpeech corpus.
Data comes from the LibriSpeech ASR corpus
File: train-clean-100.tar.gz (6.3G)(training set of 100 hours "clean" speech)
5 male speakers and 5 female speakers.
Deep Feed Forward Neural Network built with PyBrain:
Mean accuracy (over 10 speakers) is 92% with 1 second of voice data, compared to 10% for random guessing. With 3 seconds of voice data, the mean accuracy is 99%. With 200 milliseconds of data, the accuracy is 74%.
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