Metadata-Version: 1.1
Name: myprosody
Version: 11
Summary: NEW VERSION: the prosodic features of speech (simultaneous speech) compared to the features of native speech +++ spoken language proficiency level estimator
Home-page: https://github.com/Shahabks/myprosody
Author: Shahab Sabahi
Author-email: sabahi.s@mysol-gc.jp
License: MIT
Description: *** Version-11 release: if the functions of spoken language proficiency levels do not
        work on you need to do ML on your machine. Please contact me to get MLtraining.py and the relevant 
        datasets *** 
        
        *** Version-10 release: two new functions were added ***
        
        The two new functions deploy different Machine Learning algorithms to estimate the speakers' spoken 
        language proficiency levels (only the prosody aspect not semantically).  
         
        Prosody is the study of the tune and rhythm of speech and how these features contribute to meaning. 
        Prosody is the study of those aspects of speech that typically apply to a level above that of the individual 
        phoneme and very often to sequences of words (in prosodic phrases). Features above the level of the phoneme 
        (or "segment") are referred to as suprasegmentals. 
        A phonetic study of prosody is a study of the suprasegmental features of speech. At the phonetic level, 
        prosody is characterised by:
        
        1.	vocal pitch (fundamental frequency)
        2.	acoustic intensity
        3.	rhythm (phoneme and syllable duration)
        
        MyProsody is a Python library for measuring these acoustic features of speech (simultaneous speech, high entropy) 
        compared to ones of native speech. The acoustic features of native speech patterns have been observed and 
        established by employing Machine Learning algorithms. An acoustic model (algorithm) breaks recorded utterances 
        (48 kHz & 32 bit sampling rate and bit depth respectively) and detects syllable boundaries, fundamental frequency
        contours, and formants. Its built-in functions recognize/measures:
        
        	Average_syll_pause_duration
        	No._long_pause
        	Speaking-time
        	No._of_words_in_minutes
        	Articulation_rate 
        	No._words_in_minutes
        	formants_index
        	f0_index ((f0 is for fundamental frequency)
        	f0_quantile_25_index
        	f0_quantile_50_index
        	f0_quantile_75_index
        	f0_std
        	f0_max
        	f0_min
        	No._detected_vowel
        	perc%._correct_vowel
        	(f2/f1)_mean (1st and 2nd formant frequencies)
        	(f2/f1)_std
        	no._of_words
        	no._of_pauses
        	intonation_index
        	(voiced_syll_count)/(no_of_pause)
        	TOEFL_Scale_Score
        	Score_Shannon_index
        	speaking_rate
        	gender recognition 
        	speech mood (semantic analysis)
        	pronunciation posterior score 
        	articulation-rate 
        	speech rate
        	filler words 
        	f0 statistics
        	-------------
        	NEW
        	--------------
        	level (CEFR level)
        	prosody-aspects (comparison, native level)
         
        The library was developed based upon the idea introduced by Klaus Zechner et al 
        *Automatic scoring of non-native spontaneous speech in tests of spoken English* Speech Communicaion vol 
        51-2009, Nivja DeJong and Ton Wempe [1], Paul Boersma and David Weenink [2], Carlo Gussenhoven [3], 
        S.M Witt and S.J. Young [4] and Yannick Jadoul [5].
         
        Peaks in intensity (dB) that are preceded and followed by dips in intensity are considered as potential 
        syllable cores. 
        
        MyProsody is unique in its aim to provide a complete quantitative and analytical way to study acoustic 
        features of a speech. Moreover, those features could be analysed further by employing Python's 
        functionality to provide more fascinating insights into speech patterns. 
        
        This library is for Linguists, scientists, developers, speech and language therapy clinics and researchers.   
        Please note that MyProsody Analysis is currently in initial state though in active development. While the 
        amount of functionality that is currently present is not huge, more will be added over the next few months.
        
        											Installation
        											=============
        Myprosody can be installed like any other Python library, using (a recent version of) the Python package 
        manager pip, on Linux, macOS, and Windows:
        
        										pip install myprosody
        				
        or, to update your installed version to the latest release:
        										
        										pip install -u myprosody
        				
        NOTE:
        ============= 
        After installing Myprosody, download the folder called:  
        												
        												myprosody 
        
        from  https://github.com/Shahabks/myprosody
        
        and save on your computer. The folder includes the audio files folder where you will save your audio files 
        for analysis.
        
        Audio files must be in *.wav format, recorded at 48 kHz sample frame and 24-32 bits of resolution.
        
        To check how the myprosody functions behave, please check 
        											
        												EXAMPLES.pdf 
        on  https://github.com/Shahabks/myprosody 
        
        Myprosody was developed by MYOLUTIONS Lab in Japan. It is part of New Generation of Voice Recognition and Acoustic & Language modelling
        Project in MYSOLUTIONS Lab. That is planned to enrich the functionality of Myprosody by adding more advanced 
        functions.
Keywords: praat speech signal processing phonetics
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
