{ "info": { "author": "Prakhar Swarup, Saketh Sharma, Hitesh Tulsiani", "author_email": "prkhrswrp@gmail.com", "bugtrack_url": null, "classifiers": [], "description": "======================\nHotWord Detection\n======================\n\nThis library provides functionality for detecting a hotword in given audio file using MFCC features and Dynamic Time Warping (DTW) pattern matching algorithm.\n\nInstallation\n============\n\nThis `project is on pypi `_\n\nTo install from pypi:: \n\n\tpip install hotword_detection\n\n\t\nFrom this repository::\n\n\tgit clone https://github.com/sakethgsharma/HotWordDetection.git\n\tpython setup.py install\n\nUsage\n======\n\n*Example scripts*\n\nFor training a hotword, run::\n\t\n\tpython bin/trainHotword.py\n\nFor testing, run::\n\t\n\tpython bin/checkHotword.py\n\n*Supported features*\n\n- Mel Frequency Cepstral Coefficients\n- Choice of selecting any suitable hot word through appropriate training paradigm\n- Supports variable sampling frequencies\n- Amplitude based Voice Activity Detector(VAD) used during recordings to remove extraneous noise\n- Personalization using automatic DTW thresholding\n\nMFCC Features\n=============\n\nMFCC vectors are used in this module since they are the most commonly extracted features used for speech recognition systems. \n\n=============\t===========\t\nParameter \tDescription\t\n=============\t===========\t\nalpha\t\tParameter used in pre-emphasis filtering. Should be any value between 0 and 1.\nN \t\tNumber of FFT points.\nfs \t\tSampling frequency of stored audio file.\nframe_dur\tDuration of 1 speech frame.\nnum_filters\tNumber of filters used in the Mel filterbank.\nlower_freq\tLower frequency bound used for constructing filterbank.\nupper_freq\tUpper frequency bound used for constructing filterbank. 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