disvoice


Namedisvoice JSON
Version 0.1.8 PyPI version JSON
download
home_pagehttps://github.com/jcvasquezc/disvoice
SummaryPython framework designed to compute different types of features from speech files
upload_time2022-05-04 20:54:14
maintainer
docs_urlNone
authorJ. C. Vasquez-Correa
requires_python
licenseMIT
keywords speech speech features articulatory features phoneme recognition prosody praat
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # DisVoice

[![Documentation Status](https://readthedocs.org/projects/disvoice/badge/?version=latest)](https://disvoice.readthedocs.io/en/latest/?badge=latest)

![Image](https://github.com/jcvasquezc/DisVoice/blob/master/docs/logos/disvoice_logo.png?raw=true)

DisVoice is a python framework designed to compute features from speech files. Disvoice computes glottal, phonation, articulation, prosody, phonological, and features representation learnig strategies using autoencders. The features can be computed both from sustained vowels and continuous speech utterances with the aim to recognize praliguistic aspects from speech.

The features can be used in classifiers to recognize emotions, or communication capabilities of patients with different speech disorders including diseases with functional origin such as larinx cancer or nodules; craneo-facial based disorders such as hipernasality developed by cleft-lip and palate; or neurodegenerative disorders such as Parkinson's or Hungtinton's diseases.

The features are also suitable to evaluate mood problems like depression based on speech patterns.

For additional details about each feature type, and how to use DisVoice, please check

- [glottal](https://github.com/jcvasquezc/DisVoice/tree/master/glottal)
- [phonation](https://github.com/jcvasquezc/DisVoice/tree/master/phonation)
- [articulaton](https://github.com/jcvasquezc/DisVoice/tree/master/articulation)
- [prosody](https://github.com/jcvasquezc/DisVoice/tree/master/prosody) 
- [phonological](https://github.com/jcvasquezc/DisVoice/tree/master/phonological)
- [Representation learning](https://github.com/jcvasquezc/DisVoice/tree/master/replearning)


### Install

Praat should be installed first, and the executable file should be added as an environmental variable. 

For linux

```
apt-get install praat
pip install disvoice
```

or

```python setup.py install```

For Windows

Donwload the latest version of Praat from https://www.fon.hum.uva.nl/praat/download_win.html

and add the path file to the environment variables

Then

```
pip install disvoice
```

or

```python setup.py install```



Kaldi must be installed beforehand for Kaldi output  

## Reference

If you use Disvoice for research purposes, please cite the following papers, depending on the features you use:

## Glottal features

[1] Belalcázar-Bolaños, E. A., Orozco-Arroyave, J. R., Vargas-Bonilla, J. F., Haderlein, T., & Nöth, E. (2016, September). Glottal Flow Patterns Analyses for Parkinson’s Disease Detection: Acoustic and Nonlinear Approaches. In International Conference on Text, Speech, and Dialogue (pp. 400-407). Springer.


## Phonation features

[1] T. Arias-Vergara, J. C. Vásquez-Correa, J. R. Orozco-Arroyave, Parkinson's Disease and Aging: Analysis of Their Effect in Phonation and Articulation of Speech, Cognitive computation, (2017).

[2] Vásquez-Correa, J. C., et al. "Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease." Journal of communication disorders 76 (2018): 21-36.

## Articulation features

[1] Vásquez-Correa, J. C., et al. "Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease." Journal of communication disorders 76 (2018): 21-36.

[2]. J. R. Orozco-Arroyave, J. C. Vásquez-Correa et al. "NeuroSpeech: An open-source software for Parkinson's speech analysis." Digital Signal Processing (2017).

## Prosody features

[1]. N., Dehak, P. Dumouchel, and P. Kenny. "Modeling prosodic features with joint factor analysis for speaker verification." IEEE Transactions on Audio, Speech, and Language Processing 15.7 (2007): 2095-2103.

[2] Vásquez-Correa, J. C., et al. "Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease." Journal of communication disorders 76 (2018): 21-36.

## Phonological features

[1] Vásquez-Correa, J. C., et al (2019). Phonet: a Tool Based on Gated Recurrent Neural Networks to Extract Phonological Posteriors from Speech. Proc. Interspeech 2019, 549-553.

## Representaton learning-based features

[1] Vasquez-Correa, J. C., et al. (2020). Parallel Representation Learning for the Classification of Pathological Speech: Studies on Parkinson’s Disease and Cleft Lip and Palate. Speech Communication, 122, 56-67.


License
----

MIT



            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/jcvasquezc/disvoice",
    "name": "disvoice",
    "maintainer": "",
    "docs_url": null,
    "requires_python": "",
    "maintainer_email": "",
    "keywords": "speech,speech features,articulatory features,phoneme recognition,prosody,praat",
    "author": "J. C. Vasquez-Correa",
    "author_email": "juan.vasquez@fau.de",
    "download_url": "https://files.pythonhosted.org/packages/0a/33/6578b3f6912a9605ef29f1a997795df57d408dc0c10fe6741e0b619a9add/disvoice-0.1.8.tar.gz",
    "platform": null,
    "description": "# DisVoice\r\n\r\n[![Documentation Status](https://readthedocs.org/projects/disvoice/badge/?version=latest)](https://disvoice.readthedocs.io/en/latest/?badge=latest)\r\n\r\n![Image](https://github.com/jcvasquezc/DisVoice/blob/master/docs/logos/disvoice_logo.png?raw=true)\r\n\r\nDisVoice is a python framework designed to compute features from speech files. Disvoice computes glottal, phonation, articulation, prosody, phonological, and features representation learnig strategies using autoencders. The features can be computed both from sustained vowels and continuous speech utterances with the aim to recognize praliguistic aspects from speech.\r\n\r\nThe features can be used in classifiers to recognize emotions, or communication capabilities of patients with different speech disorders including diseases with functional origin such as larinx cancer or nodules; craneo-facial based disorders such as hipernasality developed by cleft-lip and palate; or neurodegenerative disorders such as Parkinson's or Hungtinton's diseases.\r\n\r\nThe features are also suitable to evaluate mood problems like depression based on speech patterns.\r\n\r\nFor additional details about each feature type, and how to use DisVoice, please check\r\n\r\n- [glottal](https://github.com/jcvasquezc/DisVoice/tree/master/glottal)\r\n- [phonation](https://github.com/jcvasquezc/DisVoice/tree/master/phonation)\r\n- [articulaton](https://github.com/jcvasquezc/DisVoice/tree/master/articulation)\r\n- [prosody](https://github.com/jcvasquezc/DisVoice/tree/master/prosody) \r\n- [phonological](https://github.com/jcvasquezc/DisVoice/tree/master/phonological)\r\n- [Representation learning](https://github.com/jcvasquezc/DisVoice/tree/master/replearning)\r\n\r\n\r\n### Install\r\n\r\nPraat should be installed first, and the executable file should be added as an environmental variable. \r\n\r\nFor linux\r\n\r\n```\r\napt-get install praat\r\npip install disvoice\r\n```\r\n\r\nor\r\n\r\n```python setup.py install```\r\n\r\nFor Windows\r\n\r\nDonwload the latest version of Praat from https://www.fon.hum.uva.nl/praat/download_win.html\r\n\r\nand add the path file to the environment variables\r\n\r\nThen\r\n\r\n```\r\npip install disvoice\r\n```\r\n\r\nor\r\n\r\n```python setup.py install```\r\n\r\n\r\n\r\nKaldi must be installed beforehand for Kaldi output  \r\n\r\n## Reference\r\n\r\nIf you use Disvoice for research purposes, please cite the following papers, depending on the features you use:\r\n\r\n## Glottal features\r\n\r\n[1] Belalc\u00c3\u00a1zar-Bola\u00c3\u00b1os, E. A., Orozco-Arroyave, J. R., Vargas-Bonilla, J. F., Haderlein, T., & N\u00c3\u00b6th, E. (2016, September). Glottal Flow Patterns Analyses for Parkinson\u00e2\u20ac\u2122s Disease Detection: Acoustic and Nonlinear Approaches. In International Conference on Text, Speech, and Dialogue (pp. 400-407). Springer.\r\n\r\n\r\n## Phonation features\r\n\r\n[1] T. Arias-Vergara, J. C. V\u00c3\u00a1squez-Correa, J. R. Orozco-Arroyave, Parkinson's Disease and Aging: Analysis of Their Effect in Phonation and Articulation of Speech, Cognitive computation, (2017).\r\n\r\n[2] V\u00c3\u00a1squez-Correa, J. C., et al. \"Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease.\" Journal of communication disorders 76 (2018): 21-36.\r\n\r\n## Articulation features\r\n\r\n[1] V\u00c3\u00a1squez-Correa, J. C., et al. \"Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease.\" Journal of communication disorders 76 (2018): 21-36.\r\n\r\n[2]. J. R. Orozco-Arroyave, J. C. V\u00c3\u00a1squez-Correa et al. \"NeuroSpeech: An open-source software for Parkinson's speech analysis.\" Digital Signal Processing (2017).\r\n\r\n## Prosody features\r\n\r\n[1]. N., Dehak, P. Dumouchel, and P. Kenny. \"Modeling prosodic features with joint factor analysis for speaker verification.\" IEEE Transactions on Audio, Speech, and Language Processing 15.7 (2007): 2095-2103.\r\n\r\n[2] V\u00c3\u00a1squez-Correa, J. C., et al. \"Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease.\" Journal of communication disorders 76 (2018): 21-36.\r\n\r\n## Phonological features\r\n\r\n[1] V\u00c3\u00a1squez-Correa, J. C., et al (2019). Phonet: a Tool Based on Gated Recurrent Neural Networks to Extract Phonological Posteriors from Speech. Proc. Interspeech 2019, 549-553.\r\n\r\n## Representaton learning-based features\r\n\r\n[1] Vasquez-Correa, J. C., et al. (2020). Parallel Representation Learning for the Classification of Pathological Speech: Studies on Parkinson\u00e2\u20ac\u2122s Disease and Cleft Lip and Palate. Speech Communication, 122, 56-67.\r\n\r\n\r\nLicense\r\n----\r\n\r\nMIT\r\n\r\n\r\n",
    "bugtrack_url": null,
    "license": "MIT",
    "summary": "Python framework designed to compute different types of features from speech files",
    "version": "0.1.8",
    "project_urls": {
        "Bug Tracker": "https://github.com/jcvasquezc/DisVoice/issues",
        "Documentation": "https://disvoice.readthedocs.io/en/latest/",
        "Download": "https://github.com/jcvasquezc/disvoice/#files",
        "Homepage": "https://github.com/jcvasquezc/disvoice",
        "Source code": "https://github.com/jcvasquezc/disvoice"
    },
    "split_keywords": [
        "speech",
        "speech features",
        "articulatory features",
        "phoneme recognition",
        "prosody",
        "praat"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "0a336578b3f6912a9605ef29f1a997795df57d408dc0c10fe6741e0b619a9add",
                "md5": "64a1e35dc308934a3c78bcb5d5ce6889",
                "sha256": "7390d91949572f6049e7d5eb6f42405622ae5727eb21a1c05882251a521d73bc"
            },
            "downloads": -1,
            "filename": "disvoice-0.1.8.tar.gz",
            "has_sig": false,
            "md5_digest": "64a1e35dc308934a3c78bcb5d5ce6889",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 16249011,
            "upload_time": "2022-05-04T20:54:14",
            "upload_time_iso_8601": "2022-05-04T20:54:14.131471Z",
            "url": "https://files.pythonhosted.org/packages/0a/33/6578b3f6912a9605ef29f1a997795df57d408dc0c10fe6741e0b619a9add/disvoice-0.1.8.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2022-05-04 20:54:14",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "jcvasquezc",
    "github_project": "disvoice",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": false,
    "requirements": [],
    "lcname": "disvoice"
}
        
Elapsed time: 2.81032s