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Updated: Sep 16, 2025

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Maximum Correntropy Linear Prediction for Voice Inverse Filtering: Theoretical Framework and Practical

Iván A Zalazar1, Gabriel A Alzamendi1, Matías Zañartu2

  • 1Institute for Research and Development on Bioengineering and Bioinformatics, CONICET-UNER, Oro Verde, Entre Ríos, Argentina.

IEEE Transactions on Audio, Speech, and Language Processing (2025)
|July 7, 2025
PubMed
Summary
This summary is machine-generated.

Maximum correntropy criterion-based linear prediction (MCLP) offers robust voice inverse filtering by downplaying inaccurate glottal closure data. This novel method improves vocal tract filter estimation without needing glottal timing information.

Keywords:
Closed phase analysisCorrentropyGlottal source estimationVoice inverse filteringWeighted linear prediction

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Area of Science:

  • Speech processing
  • Biomedical engineering
  • Signal processing

Background:

  • Voice inverse filtering estimates glottal source information noninvasively.
  • Current methods often use parametric models and linear prediction variants.
  • Linear prediction is sensitive to outliers from glottal closure events.

Purpose of the Study:

  • To investigate Maximum Correntropy Criterion-based Linear Prediction (MCLP) for voice inverse filtering.
  • To develop a robust algorithm for estimating vocal tract filter coefficients using MCLP.
  • To analyze the performance and characteristics of MCLP in voice inverse filtering.

Main Methods:

  • Developed a theoretical framework for correntropy in voice inverse filtering.
  • Proposed an iterative algorithm for robust weighted linear prediction using a data-driven optimization scheme.
  • Analyzed the impact of correntropy kernel parameters on MCLP performance.

Main Results:

  • MCLP naturally downweights samples during the glottal closed phase, improving model accuracy.
  • The MCLP method does not require prior knowledge of glottal instants or predefined weighting functions.
  • Simulations demonstrated MCLP performs comparably to or better than existing weighted linear prediction methods.

Conclusions:

  • MCLP provides a robust and data-driven approach to voice inverse filtering.
  • Its inherent insensitivity to outliers makes it suitable for handling glottal closure complexities.
  • MCLP offers a promising alternative to traditional linear prediction-based inverse filtering techniques.