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        "author": "Shahab Sabahi",
        "author_email": "sabahi.s@mysol-gc.jp",
        "bugtrack_url": null,
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        "description": "This algorithm was built for processing high-entropy speech (simultaneous free speech processing) using probabilistic \nmachine learning and deep learning models to predict spoken English language proficiency. This algorithm can measure the \u201cpronunciation\u201d, \n\"prosody\", \"use of language\" competency and latent semantic index of a user (speaker) to rate its spoken proficiency based on existing \nclassification of TOEFL scores and also compare it with the average rate of non-native and native speakers.\n\nThis is the results from two years of study whose overall achievement is an average assessment accuracy level of 72% for non-native adult speakers. \nThe correlation between the human scores and the machine scores for an overall measure of speaking was 0.86 thus proving the reliability of \nthe measure of speaking in tests. \n\n=============\nInstallation\n=============\nmy-voice-analysis can be installed like any other Python library, using (a recent version of) the Python package \nmanager pip, on Linux, macOS, and Windows:\n\n------------- pip install SpeechRater\n\t\t\t\t------------------------------\nor, to update your installed version to the latest release:\n-------------    pip install -u SpeechRater\n\t\t\t\t---------------------------------\nNOTE: \nAfter installing SpeechRater download the folder  \n-----------------speech_rate --------------\nby clicking \"download\" 0n \n---------- https://shahabks.github.io/Speech-Rater/\nand save in the directory where you will save audio files for analysis.\n\nAudio files must be in *.wav format, recorded at 48 kHz sample frame and 24 bits of resolution.\n\nTo check how the my-voice-analysis functions behave, please check \n---------------- EXAMPLES.docx on --------\n-----------------https://github.com/Shahabks/Speech-Rater\n\n\n\n",
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        "keywords": "praat speech signal processing phonetics",
        "license": "MIT",
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        "name": "SpeechRater",
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        "summary": "it measures the \u201cpronunciation\u201d, \"prosody\", \"use of language\" competency and latent semantic index of a speech and rates spoken language proficiency based on existing classification of TOEFL scores",
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