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Related Experiment Video

Updated: Jun 19, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Speech enhancement based on nonlinear models using particle filters.

Frédéric Mustière1, Miodrag Bolić, Martin Bouchard

  • 1School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada. mustiere@site.uottawa.ca

IEEE Transactions on Neural Networks
|November 4, 2009
PubMed
Summary
This summary is machine-generated.

Particle filters (PFs) enhance speech by using nonlinear neural models, outperforming linear models, especially in noisy conditions. This study explores various PF algorithms for improved speech enhancement.

Related Experiment Videos

Last Updated: Jun 19, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Area of Science:

  • Signal Processing
  • Computational Linguistics
  • Machine Learning

Background:

  • Particle filters (PFs) show promise for speech enhancement with linear models.
  • Nonlinear speech models can offer more refined representations.
  • Integrating nonlinearities into PF-based speech enhancement is an active research area.

Purpose of the Study:

  • To investigate particle filter (PF) solutions for speech enhancement using nonlinear, neural-type speech models.
  • To develop and evaluate various PF algorithms tailored for these nonlinear models.
  • To compare the performance of nonlinear PF algorithms against linear PF and traditional methods.

Main Methods:

  • Developed several global nonlinear speech models (single/multiple neurons, bias/no bias).
  • Derived corresponding particle filter (PF) solutions, incorporating Rao-Blackwellization and dual/nondual variations.
  • Handled both white and colored noise scenarios.
  • Evaluated algorithms using diverse speech/noise signals and objective quality measures.

Main Results:

  • Established a performance hierarchy among the evaluated PF algorithms.
  • The best-performing algorithms included a Rao-Blackwellized particle filter (RBPF) on a linear model and a proposed nondual, nonlinear PF with multiple neurons.
  • The neural-network-based PF consistently outperformed RBPF at low signal-to-noise ratios (SNRs).

Conclusions:

  • Nonlinear, neural-type speech models offer advantages for particle filter-based speech enhancement.
  • The proposed nonlinear PF algorithms demonstrate superior performance, particularly in low SNR environments.
  • This research advances the state-of-the-art in noise reduction for speech processing.