the analysis of voice (simultaneous speech) without the need of a transcription
Project description
My-Voice-Analysis is a Python library for the analysis of voice (simultaneous speech, high entropy)
without the need of a transcription. It breaks utterances and detects syllable boundaries, fundamental frequency contours,
and formants. Its built-in functions recognize and measures:
1. gender recognition,
2. speech mood (semantic analysis),
3. pronunciation posterior score
4. articulation-rate,
5. speech rate,
6. filler words,
7. f0 statistics.
The library was developed based upon the idea introduced by 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.
My-Voice Analysis 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 My-Voice 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
my-voice-analysis 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 my-voice-analysis
or, to update your installed version to the latest release:
pip install -u my-voice-analysis
NOTE:
After installing My-Voice-Analysis, copy the file
myspsolution.praat from
https://github.com/Shahabks/my-voice-analysis
and save in the directory where you will save audio files for analysis.
Audio files must be in *.wav format, recorded at 44 kHz sample frame and 16 bits of resolution.
To check how the my-voice-analysis functions behave, please check
EXAMPLES.docx on
https://github.com/Shahabks/my-voice-analysis.
My-Voice-Analysis was developed by MYOLUTIONS Lab in Japan. It is part of New Generation of Voice Recognition and Analysis
Project in MYSOLUTIONS Lab. That is planned to rich the functionality of My-Voice Analysis by adding more advanced
functions.
without the need of a transcription. It breaks utterances and detects syllable boundaries, fundamental frequency contours,
and formants. Its built-in functions recognize and measures:
1. gender recognition,
2. speech mood (semantic analysis),
3. pronunciation posterior score
4. articulation-rate,
5. speech rate,
6. filler words,
7. f0 statistics.
The library was developed based upon the idea introduced by 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.
My-Voice Analysis 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 My-Voice 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
my-voice-analysis 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 my-voice-analysis
or, to update your installed version to the latest release:
pip install -u my-voice-analysis
NOTE:
After installing My-Voice-Analysis, copy the file
myspsolution.praat from
https://github.com/Shahabks/my-voice-analysis
and save in the directory where you will save audio files for analysis.
Audio files must be in *.wav format, recorded at 44 kHz sample frame and 16 bits of resolution.
To check how the my-voice-analysis functions behave, please check
EXAMPLES.docx on
https://github.com/Shahabks/my-voice-analysis.
My-Voice-Analysis was developed by MYOLUTIONS Lab in Japan. It is part of New Generation of Voice Recognition and Analysis
Project in MYSOLUTIONS Lab. That is planned to rich the functionality of My-Voice Analysis by adding more advanced
functions.
Project details
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