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reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","deep-neural-networks","linguistic-analysis","linguistics","phonetics","speech-processing"],"created_at":"2026-01-14T09:09:01.635Z","updated_at":"2026-01-14T09:09:02.329Z","avatar_url":"https://github.com/jcvasquezc.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"======================\nPhonet\n======================\n\n.. image:: https://readthedocs.org/projects/phonet/badge/?version=latest\n:target: https://phonet.readthedocs.io/en/latest/?badge=latest\n:alt: Documentation Status\n\n.. image:: https://travis-ci.org/jcvasquezc/phonet.svg?branch=master\n    :target: https://travis-ci.org/jcvasquezc/phonet\n\nThis toolkit compute posteriors probabilities of phonological classes from audio files for several groups of phonemes according to the mode and manner of articulation.\n\nIf you are not sure about what phonological classes are, have a look at this\n`Phonological classes tutorial \u003chttp://research.cs.tamu.edu/prism/lectures/sp/l3.pdf\u003e`_\n\n\n`Project Documentation \u003chttp://phonet.readthedocs.org/en/latest/\u003e`_\n\n`Paper \u003cttp://dx.doi.org/10.21437/Interspeech.2019-1405\u003e`_\n\nThe list of the phonological classes available and the phonemes that are activated for each phonological class are observed in the following Table\n\n\nThe list of the phonological classes available and the phonemes that are activated for each phonological class are observed in the following Table\n\n\n==================    ================================================================================\nPhonological class    Phonemes\n==================    ================================================================================\nvocalic               /a/, /e/, /i/, /o/, /u/\nconsonantal           /b/, /tS/, /d/, /f/, /g/, /x/, /k/, /l/, /ʎ/, /m/, /n/, /p/, /ɾ/, /r/, /s/, /t/\nback                  /a/, /o/, /u/\nanterior              /e/, /i/\nopen                  /a/, /e/, /o/\nclose                 /i/, /u/\nnasal                 /m/, /n/\nstop                  /p/, /b/, /t/, /k/, /g/, /tS/, /d/\ncontinuant            /f/, /b/, /tS/, /d/, /s/, /g/, /ʎ/, /x/\nlateral               /l/\nflap                  /ɾ/\ntrill                 /r/\nvoiced                /a/, /e/, /i/, /o/, /u/, /b/, /d/, /l/, /m/, /n/, /r/, /g/, /ʎ/\nstrident              /f/, /s/, /tS/\nlabial                /m/, /p/, /b/, /f/\ndental                /t/, /d/\nvelar                 /k/, /g/, /x/\npause                 /sil/\n==================    ================================================================================\n\n\nInstallation\n============\n\n\nFrom this repository::\n\n    git clone https://github.com/jcvasquezc/phonet\n    cd phonet\n    python setup.py\n\nUsage\n=====\n\nSupported features:\n\n- Estimate probabilities of phonological classes for an audio file\n\n`Example use \u003cexample.py\u003e`_\n\nEstimation of phonological classes\n====================================\n\nEstimate the phonological classes using the BGRU models for an audio file or for a folder that contains audio files inside::\n\n      python\n      phon=Phonet([phonclass])\n      get_phon_wav(self, audio_file, feat_file, plot_flag=True)\n\n============= ===========\nParameters     Description\n============= ===========\naudio_file    file audio (.wav) sampled at 16 kHz\nfeat_file     file (.csv) to save the posteriors for the phonological classes\nphonclass     list of phonological classes to be evaluated\n              The list of phonological classes include:\n              \"consonantal\", \"back\", \"anterior\", \"open\", \"close\", \"nasal\", \"stop\",\n              \"continuant\",  \"lateral\", \"flap\", \"trill\", \"voice\", \"strident\",\n              \"labial\", \"dental\", \"velar\", \"pause\", \"vocalic\" or \"all\"\nplot_flag     True or False, whether you want plots of phonological classes or not\nreturns\t\t\t  It crates the feat_file with the estimation of the phonological classes for each time-frame of the audio file.\n============= ===========\n\nTraining\n====================================\n\nIf you want to train Phonet in your own language, or specific phonological classes that are not defined here, please refer to the folder `train \u003chttps://github.com/jcvasquezc/phonet/tree/master/train\u003e`_ and follow the instructions there.\n\nIf you experienced problems with the Training process, please send me an email `\u003cjuan.vasquez@fau.de\u003e`\n\n\nReference\n==================================\n\nPhonet is available for research purposes\n\nIf you use Phonet, please cite the following paper.\n\n@inproceedings{Vasquez-Correa2019,\n  author={J. C. Vásquez-Correa and P. Klumpp and J. R. Orozco-Arroyave and E. N\\\"oth},\n  title={{Phonet: A Tool Based on Gated Recurrent Neural Networks to Extract Phonological Posteriors from Speech}},\n  year=2019,\n  booktitle={Proc. Interspeech 2019},\n  pages={549--553},\n  doi={10.21437/Interspeech.2019-1405},\n  url={http://dx.doi.org/10.21437/Interspeech.2019-1405}\n}\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcvasquezc%2Fphonet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjcvasquezc%2Fphonet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcvasquezc%2Fphonet/lists"}