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Biomimetic multi-resolution analysis for robust speaker recognition.

Sridhar Krishna Nemala1, Dmitry N Zotkin2, Ramani Duraiswami2

  • 1Department of Electrical and Computer Engineering, Center for Language and Speech Processing, Johns Hopkins University, Baltimore, MD, USA.

EURASIP Journal on Audio, Speech, and Music Processing
|December 15, 2018
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Summary
This summary is machine-generated.

This study introduces a biomimetic approach for speech processing, creating robust speaker recognition features inspired by human auditory systems. This method enhances performance in noisy conditions, improving automatic speech processing.

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Area of Science:

  • Speech Processing
  • Biomimetic Engineering
  • Auditory Neuroscience

Background:

  • Human auditory systems excel at sound source classification despite noise, unlike current engineering systems.
  • Automatic speech processing systems degrade significantly with background noise and channel distortions.
  • Biological auditory machinery offers insights for improving speech processing robustness.

Purpose of the Study:

  • To develop a biologically-motivated feature representation for speaker verification.
  • To enhance the robustness of automatic speech processing systems in adverse acoustic conditions.
  • To explore spectro-temporal speech attributes for reliable speaker recognition.

Main Methods:

  • A multi-resolution speaker information representation was derived from spectro-temporal speech attributes.
  • A computationally-efficient, intricate analysis of speech signals was performed.
  • The proposed features were evaluated on the NIST SRE 2010 speaker verification dataset.

Main Results:

  • The biomimetic features demonstrated significant robustness against non-stationary noise.
  • The approach showed improved performance in the presence of reverberation.
  • The novel features offer a new framework for reliable speaker recognition.

Conclusions:

  • Biomimetic feature extraction provides a promising direction for robust speaker recognition.
  • This approach can significantly improve automatic speech processing in challenging environments.
  • The study highlights the potential of leveraging human auditory principles in engineering systems.