The neurogram matching similarity index (NMSI) for the assessment of similarities among neurograms

Michael Drews, Michele Nicoletti, Werner Hemmert, Stefano Rini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper a new similarity index for neurograms is proposed. This index is inspired by the Needleman-Wunsch algorithm which determines the minimum number of operations to transform a vector into another in terms of insertions, deletions and substitutions. The Needleman-Wunsch algorithm can be extended to the two dimensional case and the number of transformations required to change a matrix into another is used to define a measure of similarity. This similarity measure is applied to neurograms and optimized to perform prediction of speech intelligibility in noise. Word recognition scores for for speech samples in noise are evaluated using the proposed similarity index, showing a clear improvement in speech intelligibility estimation with respect to other neurogram similarity metrics in the literature. The proposed similarity index is not restricted to a certain time resolution and could serve to evaluate neurogram similarity with respect to temporal fine structure in future.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages1162-1166
Number of pages5
DOIs
StatePublished - 18 Oct 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: 26 May 201331 May 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period26/05/1331/05/13

Keywords

  • edit distance
  • neurogram
  • similarity measure
  • speech intelligibility

Fingerprint Dive into the research topics of 'The neurogram matching similarity index (NMSI) for the assessment of similarities among neurograms'. Together they form a unique fingerprint.

  • Cite this

    Drews, M., Nicoletti, M., Hemmert, W., & Rini, S. (2013). The neurogram matching similarity index (NMSI) for the assessment of similarities among neurograms. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings (pp. 1162-1166). [6637833] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2013.6637833