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        "author": "Shahab Sabahi",
        "author_email": "sabahi.s@mysol-gc.jp",
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        "description": "*** Version-20 release: if the functions of spoken language proficiency levels do not\nwork on you need to do ML on your machine. Please contact me to get MLtraining.py and the relevant \ndatasets *** \n\n*** Version-10 release: two new functions were added ***\n\nThe two new functions deploy different Machine Learning algorithms to estimate the speakers' spoken \nlanguage proficiency levels (only the prosody aspect not semantically).  \n\nProsody is the study of the tune and rhythm of speech and how these features contribute to meaning. \nProsody is the study of those aspects of speech that typically apply to a level above that of the individual \nphoneme and very often to sequences of words (in prosodic phrases). Features above the level of the phoneme \n(or \"segment\") are referred to as suprasegmentals. \nA phonetic study of prosody is a study of the suprasegmental features of speech. At the phonetic level, \nprosody is characterised by:\n\n1.\tvocal pitch (fundamental frequency)\n2.\tacoustic intensity\n3.\trhythm (phoneme and syllable duration)\n\nMyProsody is a Python library for measuring these acoustic features of speech (simultaneous speech, high entropy) \ncompared to ones of native speech. The acoustic features of native speech patterns have been observed and \nestablished by employing Machine Learning algorithms. An acoustic model (algorithm) breaks recorded utterances \n(48 kHz & 32 bit sampling rate and bit depth respectively) and detects syllable boundaries, fundamental frequency\ncontours, and formants. Its built-in functions recognize/measures:\n\n\tAverage_syll_pause_duration\n\tNo._long_pause\n\tSpeaking-time\n\tNo._of_words_in_minutes\n\tArticulation_rate \n\tNo._words_in_minutes\n\tformants_index\n\tf0_index ((f0 is for fundamental frequency)\n\tf0_quantile_25_index\n\tf0_quantile_50_index\n\tf0_quantile_75_index\n\tf0_std\n\tf0_max\n\tf0_min\n\tNo._detected_vowel\n\tperc%._correct_vowel\n\t(f2/f1)_mean (1st and 2nd formant frequencies)\n\t(f2/f1)_std\n\tno._of_words\n\tno._of_pauses\n\tintonation_index\n\t(voiced_syll_count)/(no_of_pause)\n\tTOEFL_Scale_Score\n\tScore_Shannon_index\n\tspeaking_rate\n\tgender recognition \n\tspeech mood (semantic analysis)\n\tpronunciation posterior score \n\tarticulation-rate \n\tspeech rate\n\tfiller words \n\tf0 statistics\n\t-------------\n\tNEW\n\t--------------\n\tlevel (CEFR level)\n\tprosody-aspects (comparison, native level)\n\nThe library was developed based upon the idea introduced by Klaus Zechner et al \n*Automatic scoring of non-native spontaneous speech in tests of spoken English* Speech Communicaion vol \n51-2009, Nivja DeJong and Ton Wempe [1], Paul Boersma and David Weenink [2], Carlo Gussenhoven [3], \nS.M Witt and S.J. Young [4] and Yannick Jadoul [5].\n\nPeaks in intensity (dB) that are preceded and followed by dips in intensity are considered as potential \nsyllable cores. \n\nMyProsody is unique in its aim to provide a complete quantitative and analytical way to study acoustic \nfeatures of a speech. Moreover, those features could be analysed further by employing Python's \nfunctionality to provide more fascinating insights into speech patterns. \n\nThis library is for Linguists, scientists, developers, speech and language therapy clinics and researchers.   \nPlease note that MyProsody Analysis is currently in initial state though in active development. While the \namount of functionality that is currently present is not huge, more will be added over the next few months.\n\n\t\t\t\t\t\t\t\t\t\t\tInstallation\n\t\t\t\t\t\t\t\t\t\t\t=============\nMyprosody 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\t\t\t\t\t\t\t\t\t\tpip install myprosody\n\nor, to update your installed version to the latest release:\n\n\t\t\t\t\t\t\t\t\t\tpip install -u myprosody\n\nNOTE:\n============= \nAfter installing Myprosody, download the folder called:  \n\n\t\t\t\t\t\t\t\t\t\t\t\tmyprosody \n\nfrom  https://github.com/Shahabks/myprosody\n\nand save on your computer. The folder includes the audio files folder where you will save your audio files \nfor analysis.\n\nAudio files must be in *.wav format, recorded at 48 kHz sample frame and 24-32 bits of resolution.\n\nTo check how the myprosody functions behave, please check \n\n\t\t\t\t\t\t\t\t\t\t\t\tEXAMPLES.pdf \non  https://github.com/Shahabks/myprosody \n\nMyprosody was developed by MYOLUTIONS Lab in Japan. It is part of New Generation of Voice Recognition and Acoustic & Language modelling\nProject in MYSOLUTIONS Lab. That is planned to enrich the functionality of Myprosody by adding more advanced \nfunctions.\n\n",
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        "keywords": "praat speech signal processing phonetics",
        "license": "MIT",
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        "name": "myprosody",
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        "summary": "NEW VERSION: the prosodic features of speech (simultaneous speech) compared to the features of native speech +++ spoken language proficiency level estimator",
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