VowSpace: A vowel formant analysis application

VowSpace is an open-source desktop application developed with the aim of acquiring, visualizing, normalizing, comparing, and linguistically analyzing vowel formant frequencies from audio files and/or datasets.

In phonetic and sociophonetic research, analyzing vowel formants is essential for understanding language variation, speaker physiology, and vowel space. Researchers often use general-purpose software like R or Praat, which require advanced skills and time for custom visualizations. VowSpace was created as an open-source tool for vowel space plotting, combining raw and normalized data. While R packages like phonR and vowelPlot, and Praat offer similar functions, they either assume programming expertise or lack accessible GUI features for normalization and visualization.

YOU MAY HAVE A LOOK AT THE TUTORIALS I PROVIDED AT TUTORIAL INSTRUCTIONS! :)

Version Log

VowSpace v1.4.2 Release Notes (Latest version)

New Features:

• Added new normalization methods: Nearey1, Nearey2, and Bark Difference Metric.
• Introduced additional scale conversion options: Bark, Log, Mel, and ERB.
• Enabled the NavigationToolbar in Audio Analysis Tools, which allows users to:
  – Zoom in/out
  – Change the color palette
  – Modify visualization settings
  – Save with different configurations
• Introduced a brand-new application icon that I drew myself. :)

Improvements:

• Modularized the previously monolithic codebase into a more structured, maintainable components.
• Significantly improved the overall code organization.
• Made the DataFrame Editor window a bit larger for better visibility and usability.

Documentation:

• Added comprehensive tutorials to the GitHub repository to guide users through key features and usage.

VowSpace v1.4.1 Release Notes (Previous version)

Bug Fixes:

• Fixed an issue where axes were inverted upon data import.
• Fixed a crash that occurred when the canvas size was below 1 pixel.

New Features:

• Added Ellipses and Qhulls to visually group related data points for improved readability.
• Introduced a "Group by Vowel" option: previously, data points were connected based on the speaker; now users can optionally connect same vowels across different speakers.

Improvements:

• Increased the default canvas size for better visualization.
• Set the default and minimum application window size to 800×800 pixels.
• Changed the default image export resolution from 600 to 1200 DPI for higher-quality output.
• Rearranged menu items for more intuitive navigation.

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Installation

VowSpace is currently available as a portable application.

macOS

1. Download the latest .app file from the GitHub Releases page.
2. Once downloaded, you can launch VowSpace.
3. If you encounter a security warning on first launch:
   • Right-click (or Ctrl-click) the VowSpace app and select Open.
   • Confirm the prompt from macOS Gatekeeper.
4. You may drag the VowSpace .app icon into your Applications folder.

Windows

1. Download the latest .exe file from the GitHub Releases page.
2. Once downloaded, you can launch VowSpace.

Linux (Debian tested)

1. Download the latest executible file from the GitHub Releases page.
2. Once downloaded, you can launch VowSpace.

(Some info on the Linux version: I will be working on developing a better version of VowSpace for other Linux distros, for now, I could only test it on minimal Debian. The app icon does not seem to work, and I am currently working on a better experience for Linux users!)

Run the Python application directly

Alternatively (and I highly encourage you to do so!), you may also clone this repository and run the application directly from source. This is ideal for developers or researchers who wish to experiment with or contribute to the code.

git clone https://github.com/alicagank/VowSpace.git
cd VowSpace
python main.py

If you choose to run the application from source, you’ll need Python 3.8 or higher and the following Python packages:

• PyQt5
• matplotlib
• pandas
• numpy
• scipy
• praat-parselmouth

All dependencies are listed in the requirements.txt file. To install them automatically, run:

pip install -r requirements.txt

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About VowSpace

Vowel plotting and normalization are of utmost importance when dealing with data for many subfields of linguistics, and the absence of a user-friendly application for these specific purposes has led to the emergence of VowSpace as a solution to this gap.

Vowel Plotting in VowSpace

VowSpace uses the Matplotlib (3.8.2) (Hunter, 2007) library to draw a canvas and visualize the data. When plotting vowel formants, VowSpace utilizes a rectangular template with f1 value on the rightmost side and f2 value on the bottommost side of the screen with rulers on the opposite sides of the values. The vowels that belong to different sources are represented with points with different colors.

Vowel Normalization in VowSpace

VowSpace provides options for normalizing vowel formants under the “Normalization Settings” menu. In its current version, several normalization and frequency scale conversion methods have been implemented to facilitate cross-speaker comparison and perceptual modeling.

Lobanov (Z-score Normalization)

An implementation of the Lobanov normalization method originally proposed by Lobanov (1971) and later adopted by Nearey (1977) and Adank et al. (2004). This method removes speaker-dependent anatomical differences by z-scoring the formants.

F_n[V] = (F_n[V] – MEAN_n) / S_n

Bark Difference Metric

Proposed by Traunmüller (1997), this method transforms formant values to the Bark scale and computes perceptual distance metrics such as Z3–Z1 and Z2–Z1.

Z_i = 26.81 / (1 + (1960 / F_i)) – 0.53

Nearey1 (Log-Mean Normalization)

This vowel-intrinsic, vowel-extrinsic method normalizes each formant by subtracting the log of the speaker-specific mean of all vowels, as described in Nearey (1977).

F_n[V] = log(F_n[V]) – log(mean(F_n_all_vowels))

Nearey2 (Shared Log-Mean Normalization)

A variation of the Nearey1 method, this approach subtracts a shared log-mean across all formants from each log-transformed formant, minimizing intra-speaker variation while maintaining cross-formant coherence.

F_n[V] = log(F_n[V]) – mean(log(F_1, F_2, ..., F_n))

Log-Scale Transformation

Applies a base-10 logarithmic transformation to raw formant values to account for the logarithmic nature of human auditory perception.

F_log = log10(F)

Mel-Scale Transformation

Converts formants to the Mel scale, commonly used in speech processing and auditory modeling.

F_mel = 2595 × log10(1 + F / 700)

Bark-Scale Transformation

Also based on human auditory resolution, the Bark transform maps frequencies to critical bands.

Z = 26.81 / (1 + 1960 / F) – 0.53

ERB-Scale Transformation

Transforms frequencies to the Equivalent Rectangular Bandwidth (ERB) scale, used in auditory models to simulate cochlear filtering.

F_erb = 21.4 × log10(1 + 0.00437 × F)

All normalization methods are implemented in Python based on the work of Remirez (2022), and adapted using resources such as phonR (Drammock, 2022).

Audio Analysis Tools

Audio Analysis Tools is a separate window that the user can access through the VowSpace’s main user interface. Then the user can read an audio file and get useful information about it such as intensity, pitch and vowel formant frequencies (f1-f4). All of this is donw by the Parselmouth library, an awesome interface of Praat for Python!

In the most current stage of development, the user is able to add the formant frequencies to the main visualizer window on any given t to the VowSpace interface by right-clicking on the plot on the audio analysis window.

Intensity

Pitch

Vowel Formant Frequencies

DataFrame Editor

DataFrame Editor is a separate window to make small adjustments on the data that you’re working on without relying on any other application. When you use the ‘Save Changes’ function, the scatterplot automatically updates with the latest data. The altered data can also be saved as a separate spreadsheet through the ‘Save Data As…’ action.

DataFrame Editor UI

Data Table Format

The minimum data table for any data to be read by VowSpace is as follows:

vowel   f1   f2   speaker
/æ/     123  1234 Markus

The only necessary rows are ‘vowel’, ‘f1’, ‘f2’, and ‘speaker’. When any data is inputted through the user interface, a dataframe is created with this information. Columns like ‘bark_f1’ for the Bark metric, logarithmic values like ‘log_f1’ and z-scores like ‘zsc_f1’ are also supported.

IPA Keyboard

As phoneticians, we love the IPA (International Phonetic Alphabet)! There is a dedicated window to input some vowels on the IPA as well!

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Acknowledgement

I am not formally trained as a programmer and am the sole developer of this application, so there may be bugs, and parts of the code might appear amateurish. VowSpace is still in development, and I am continuously adding new features as I learn new methods and techniques. I am doing my best to keep VowSpace updated and actively invite researchers to try it out and provide feedback.

However, please double-check the outputs before using them in research or publications.

I initially developed VowSpace for a research project I worked on as part of a university assignment — specifically to create a vowel plot. Instead of learning R for that purpose, I decided to build something reusable and extensible.

Disclaimer:

This application is provided "AS IS", without warranty of any kind, express or implied. The developer assumes no responsibility for any errors or inaccuracies in the software or its output. Use at your own risk and play with the code! If you encounter any errors, please open an issue, and I would gladly help you out! :)

Community Guidelines:

VowSpace is an open-source project developed and maintained by a single researcher. I welcome feedback, suggestions, and contributions from the community to help improve the tool and ensure its usefulness to linguists, phoneticians, forensic scientists and language researchers.

1. Contributing

If you would like to contribute to the development of VowSpace by improving code, fixing bugs, adding features, or enhancing documentation — please follow these steps:

• Fork the repository on GitHub.
• Create a new branch for your feature or fix.
• Commit your changes with clear messages.
• Submit a Pull Request (PR) with a short description of your contribution.

Before contributing, please make sure that your code is readable (the more comments, the better) and follows general Python best practices. If you're unsure whether an idea fits the scope of the project, feel free to open an issue first to discuss it. You can also always write me an email!

2. Reporting Issues or Bugs

If you encounter any bugs or unexpected behavior please:

• Open an Issue on GitHub.
• Include as much detail as possible (e.g., OS, Python version, error messages, screenshots).
• Clearly describe what you expected to happen and what actually occurred.

This helps me understand and resolve the issue more efficiently.

3. Seeking Support

If you need help using VowSpace or have questions about specific features:

• Check the README and the examples on the dedicated page on my website VowSpace page for usage instructions and examples.
• Open a GitHub issue with your question or request.
• You may also reach out to me directly via email.

Please note that I am a solo developer and a full-time student/researcher. I may not respond immediately, but I will do my best to help as soon as possible.

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References

• Aydın, Ö., & Uzun, İ. P. (2020). Ünlü formant normalizasyonu: R programlama dilinde bir uygulama. In İ. P. Uzun (Ed.), Kuramsal ve uygulamalı sesbilim (pp. 297–322). Seçkin Yayıncılık.

• Bladon, R. A. W., Henton, C. G., & Pickering, J. B. (1984). Towards an auditory theory of speaker normalization. Language & Communication, 4(1), 59–69. https://doi.org/10.1016/0271-5309(84)90019-3

• Boersma, P. (2001). Praat, a system for doing phonetics by computer. Glot International, 5(9/10), 341–345.

• Clopper, C. G. (2009). Computational methods for normalizing acoustic vowel data for talker differences. Language and Linguistics Compass, 3(6), 1430–1442. https://doi.org/10.1111/j.1749-818X.2009.00165.x

• Fant, G. (1975). Non-uniform vowel normalization. Speech Transmission Laboratory Quarterly Progress and Status Report, 16, 1–19.

• Gelfer, M. P., & Bennett, Q. E. (2013). Speaking Fundamental Frequency and Vowel Formant Frequencies: Effects on Perception of Gender. Journal of Voice, 27(5), 556–566. https://doi.org/10.1016/j.jvoice.2012.11.008

• Harris, C. R., et al. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2

• Heeringa, W., & Van de Velde, H. (2018). Visible Vowels: a Tool for the Visualization of Vowel Variation. In Proceedings CLARIN Annual Conference 2018, Pisa, Italy. CLARIN ERIC.

• Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55

• Jadoul, Y., Thompson, B., & de Boer, B. (2018). Introducing Parselmouth: A Python interface to Praat. Journal of Phonetics, 71, 1–15. https://doi.org/10.1016/j.wocn.2018.07.001

• Jones, D. (1917). Speech sounds: Cardinal vowels. The Gramophone.

• Joos, M. (1948). Acoustic phonetics. Language, 23, 5–136.

• Klatt, D. H. (1989). Review of selected models of speech perception. In W. Marlsen-Wilson (Ed.), Lexical representation and process (pp. 169–226). MIT Press.

• Ladefoged, P., & Broadbent, D. E. (1957). Information conveyed by vowels. The Journal of the Acoustical Society of America, 29(1), 98–104. https://doi.org/10.1121/1.1908694

• Pfitzinger, H. R., & Niebuhr, O. (2011). Historical development of phonetic vowel systems: The last 400 years. ICPhS XVII, 160–163.

• R Core Team. (2021). R: A language and environment for statistical computing [Software]. R Foundation for Statistical Computing, Vienna, Austria.

• Remirez, E. (2022, October 20). Vowel plotting in Python. In Linguistics Methods Hub. Zenodo. https://doi.org/10.5281/zenodo.7232005

• Studdert-Kennedy, M. (1964). The perception of speech. In T. A. Sebeok (Ed.), Current trends in linguistics (pp. 2349–2385). Mouton.

• Thomas, E. R., & Kendall, T. (2007). NORM: The vowel normalization and plotting suite. Online Resource

• The pandas development team. (2024). pandas-dev/pandas: Pandas (v2.2.2). Zenodo. https://doi.org/10.5281/zenodo.10957263

• Van Rossum, G., & Drake Jr., F. L. (1995). Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam.

• Virtanen, P., et al. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), 261–272. https://doi.org/10.1038/s41592-019-0686-2

• Watt, D., Fabricius, A. H., & Kendall, T. (2010). More on vowels: Plotting and normalization. In Sociophonetics: A Student’s Guide (pp. 107–118). Routledge.

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Attribution

You can cite VowSpace as:

• Kaya, A. Ç. (2024). VowSpace: A vowel formant analysis application [Poster presentation]. 37th National Linguistics Congress, Kocaeli, Türkiye. The Linguistics Association.

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For more information, examples, and to download the application, you may visit the VowSpace page or use the Releases feature on GitHub.