Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Speaker normalization of static and dynamic vowel spectral features.

S A Zahorian1, A J Jagharghi

  • 1Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, Virginia 23508-0369.

The Journal of the Acoustical Society of America
|July 1, 1991
PubMed
Summary

Speaker normalization techniques significantly improve automatic vowel classification. Both linear transformation and polynomial warping enhance spectral features, leading to higher accuracy rates for formants and DCTCs.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Spectral-shape features versus formants as acoustic correlates for vowels.

The Journal of the Acoustical Society of America·1993
Same author

Vibrotactile frequency for encoding a speech parameter.

The Journal of the Acoustical Society of America·1977
See all related articles

Area of Science:

  • Speech processing
  • Acoustic phonetics
  • Machine learning for speech

Background:

  • Speaker variability in acoustic features poses challenges for automatic speech recognition.
  • Vowel spectral features, including formants and DCTCs, are crucial for distinguishing vowels.
  • Existing methods often struggle with speaker-independent vowel identification.

Purpose of the Study:

  • To evaluate two speaker normalization methods for vowel spectral features.
  • To assess the impact of normalization on automatic vowel classification accuracy.
  • To compare the effectiveness of different spectral parameters and feature types (static vs. dynamic).

Main Methods:

  • Speaker normalization using multivariable linear transformation.
  • Speaker normalization using polynomial frequency warping.

Related Experiment Videos

  • Evaluation with formants and DCTCs (Discrete Cosine Transform Coefficients) as spectral parameters.
  • Testing static and dynamic features, with and without fundamental frequency (F0).

    • Main Results:

    • Both normalization methods consistently increased automatic vowel classification rates across all conditions.
    • Linear transformation slightly outperformed polynomial warping.
    • Normalized DCTCs trajectories achieved the highest classification rate (91%).
    • Adding F0 improved non-normalized features but yielded diminishing returns with normalized features.

    Conclusions:

    • Speaker normalization is effective in improving automatic vowel recognition.
    • Linear transformation is a slightly more effective normalization technique.
    • Dynamic features combined with normalization show the most promise for robust vowel classification.