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On pre-image iterations for speech enhancement.

Christina Leitner1, Franz Pernkopf2

  • 1JOANNEUM RESEARCH Forschungsgesellschaft mbH, DIGITAL - Institute for Information and Communication Technologies, Steyrergasse 17, Graz, 8010 Austria.

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|June 19, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces kernel PCA and pre-image iterations for speech enhancement, adapting kernel variance for optimal de-noising. Pre-image iterations notably improved word recognition accuracy in noisy conditions.

Keywords:
Automatic speech recognitionKernel PCASpeech de-noisingSpeech enhancement

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Audio Engineering

Background:

  • Speech enhancement is crucial for improving intelligibility in noisy environments.
  • Kernel PCA and pre-image iterations offer advanced signal processing techniques.
  • Adapting parameters like kernel variance is key for effective de-noising across varying signal-to-noise ratios (SNRs).

Purpose of the Study:

  • To apply kernel PCA and derive pre-image iterations for speech enhancement.
  • To develop a method for adapting kernel variance based on noise estimates for arbitrary SNRs.
  • To evaluate the performance of these novel methods against established techniques using objective and subjective measures.

Main Methods:

  • Kernel PCA and pre-image iterations utilizing a Gaussian kernel.
  • A novel method for deriving kernel variance from noise estimates.
  • Comparison with generalized subspace method, spectral subtraction, and MMSE log-spectral amplitude estimator.

Main Results:

  • Kernel PCA and pre-image iterations achieved performance comparable to reference methods on PEASS scores.
  • Pre-image iterations significantly improved word recognition accuracy compared to unprocessed and generalized subspace method processed speech.
  • The proposed methods demonstrated effectiveness across various noise types and SNRs (0-15 dB).

Conclusions:

  • Kernel PCA and pre-image iterations are effective speech enhancement techniques.
  • The adaptive kernel variance method allows for robust performance across different noise conditions.
  • Pre-image iterations show particular promise for improving automatic speech recognition performance.