Radiological reporting based on voice recognition

  • G. Antoniol
  • R. Fiutem
  • R. Flor
  • G. Lazzari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 753)


Speech recognition has proved to be a natural interaction modality and an effective technology for medical reporting, in particular in the speciality of radiology. High-volume text creation requirement and the complex structure of these texts make voice technologies useful. By employing speech, professionals in the field can generate reports and do so at a speed that approaches traditional dictation methods.

However, the integration of speech recognition in a user interface creates new problems: speech recognizers may introduce errors and moreover they should be adaptable to spoken language variations.

This paper describes a radiological reporting system and the related motivations for the use of the speech modality. A preliminary evaluation of the system has shown that, on average, although text recalling functions and keyword shortcuts are available, more than two thirds of a radiological report are generated by means of dictation.


Recognition Rate Speech Recognition Language Model Automatic Speech Recognition Radiological Report 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    G. Antoniol, F. Brugnara, F. Dalla Palma, G. Lazzari, and E. Moser A.RE.S.: An interface for automatic reporting by speech. In Proceedings of the European Conference on Speech Communication and Technology, Genova, Italy, 1991.Google Scholar
  2. 2.
    L. R. Bahl, F. Jelinek, and R. L. Mercer. A maximum likelihood approach to continuous speech recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-5(2):179–190, 1983.Google Scholar
  3. 3.
    L. R. Bahl, F. Jelinek, and R. L. Mercer. A Maximum Likelihood Approach to Continuous Speech Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(2):179–190, March 1983.Google Scholar
  4. 4.
    J. K. Baker. Trainable Grammars for Speech Recognition. In Proceedings of the Spring Conference of the Acoustical Society of America, 1979.Google Scholar
  5. 5.
    H. Cerf-Danon, S. DeGennaro, M. Ferretti, J.Gonzalez, and E. Keppel. Tangora — a large vocabulary speech recognition system for five languages. In Proceedings of the European Conference on Speech Communication and Technology, pages 215–218, Genova, Italy, September 1991.Google Scholar
  6. 6.
    M. Grice and B. Barry. Esprit project 2589 (sam) multi-lingual speech input/output assessment, methodology and standardisation, 1985. Doc. SAM-UC-149.Google Scholar
  7. 7.
    R. Joseph. Large vocabulary voice-to-text systems for medical reporting. Speech Technology, 4(4):49–51, 1989.Google Scholar
  8. 8.
    L. F. Lamel, R. H. Kassel, and S. Seneff. Speech Database Development: Design and Analysis of the Acoustic-Phonetic Corpus. In Proceedings of the DARPA Speech Recognition Workshop, 1986.Google Scholar
  9. 9.
    J.A. Larson. Interactive software: tools for building interactive user interfaces. Prentice-Hall, Englewood Cliffs, NJ, 1992.Google Scholar
  10. 10.
    H. Ney and U. Essen. On Smoothing Techniques for Bigram-Based Natural Language Modelling. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pages 825–828, Toronto, Canada, 1991.Google Scholar
  11. 11.
    David S. Pallett. Performance assessment of automatic speech recognizers, 1985. Journal of Research of the National Bureau of Standards.Google Scholar
  12. 12.
    A. I. Rudnicky and M. H. Sakamoto. Transcription Conventions and Evaluation Techniques for Spoken Language System Research. Technical Report 9204-11, School of Computer Science, CMU, Pittsburgh, PA, 1989.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • G. Antoniol
    • 1
  • R. Fiutem
    • 1
  • R. Flor
    • 1
  • G. Lazzari
    • 1
  1. 1.IRSTTrentoItaly

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