Community

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Publications

This is a list of work describing, using, or referencing the data available in the Ultrasuite repository.

Database papers

  • Ribeiro, M. S., Sanger, J., Zhang, J.-X., Eshky, A., Wrench, A., Richmond, K.,& Renals, S. (2021). TaL: a synchronised multi-speaker corpus of ultrasound tongue imaging, audio, and lip videos. Proceedings of the IEEE Workshop on Spoken Language Technology (SLT). Shenzhen, China.[Paper]
  • Eshky, A., Ribeiro, M. S., Cleland, J., Richmond, K., Roxburgh, Z., Scobbie, J., & Wrench, A. (2018) Ultrasuite: A repository of ultrasound and acoustic data from child speech therapy sessions. Proceedings of INTERSPEECH. Hyderabad, India. [Paper]

Applications

  • Csapó, T. G., & Xu, K. (2020). Quantification of Transducer Misalignment in Ultrasound Tongue Imaging. Proc. Interspeech.[Paper] [Code]
  • Zhu, J., Styler, W., & Calloway, I. (2020) MTracker-a CNN-based tool for automatic tracking of tongue contours. UltraFest, Indiana, USA. [Paper] [Code]
  • Zhu, J., Styler, W., & Calloway, I. (2019). A CNN-based tool for automatic tongue contour tracking in ultrasound images. arXiv preprint arXiv:1907.10210. [Paper] [Code]
  • Eshky, A., Ribeiro, M. S., Richmond, K., & Renals, S. (2019). Synchronising audio and ultrasound by learning cross-modal embeddings. Proc. Interspeech. Graz, Austria. [Paper] [Code].
  • Ribeiro, M. S., Eshky, A., Richmond, K., & Renals, S. (2019). Ultrasound tongue imaging for diarization and alignment of child speech therapy sessions. Proc. Interspeech. Graz, Austria. [Paper] [Code]
  • Ribeiro, M. S., Eshky, A., Richmond, K., & Renals, S. (2019). Speaker-independent classification of phonetic segments from raw ultrasound in child speech. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1328-1332). [Paper].
  • Shahin, M., Ahmed, B., Smith, D. V., Duenser, A., & Epps, J. (2019). Automatic Screening of Children with Speech Sound Disorders Using Paralinguistic Features. In IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-5).[Paper (not open access)].
  • Shahin, M., Zafar, U., & Ahmed, B. (2019). The Automatic Detection of Speech Disorders in Children: Challenges, Opportunities, and Preliminary Results. IEEE Journal of Selected Topics in Signal Processing, 14(2), 400-412. [Paper (not open access)].
  • Ribeiro, M. S., Eshky, A., Richmond, K., & Renals, S. (2018). Towards robust word alignment of child speech therapy sessions. UK Speech, Dublin, Ireland. [Abstract] [Poster]

References

  • Mozaffari, M. H., Sankoff, D., & Lee, W. S. (2019). Ultrasound tongue contour extraction using BowNet network: A deep learning approach. In Proceedings of Meetings on Acoustics 178, ASA (Vol. 39, No. 1, p. 020001). Acoustical Society of America. [Paper]
  • Mozaffari, M. H., Ratul, M., Rab, A., & Lee, W. S. (2019). Irisnet: Deep learning for automatic and real-time tongue contour tracking in ultrasound video data using peripheral vision. arXiv preprint arXiv:1911.03972.
  • Hewer, A., Steiner, I., & Richmond, K. (2019). Analysis of coarticulation using EMA data with a statistical shape space model of the tongue. Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2019, 296-303. [Paper].
  • Scobbie, J. M., & Ma, J. (2019). Say again? Individual articulatory strategies for producing a clearly-spoken minimal pair wordlist. Proc. 19th ICPhS. [Paper]

Publications list last update: November 2020