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Related Concept Videos

Calibration Curves: Linear Least Squares01:20

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Related Experiment Video

Updated: Jan 5, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Forensic Speaker Verification Using Ordinary Least Squares.

Thyago J Machado1, Jozue Vieira Filho2, Mario A de Oliveira3

  • 1Campus of Ilha Solteira, São Paulo State University (UNESP), São Paulo 15385-000, Brazil. tmachado@forenselab.com.

Sensors (Basel, Switzerland)
|October 30, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new speaker verification method using linear predictive coding (LPC) and ordinary least squares (OLS) for forensic analysis in Brazil. The technique achieved a 100% hit rate, offering an accurate and quick tool for speaker identification.

Keywords:
forensic phoneticsforensic speaker comparisonlinear predictive coding (LPC)ordinary least squares (OLS)voice processing

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Area of Science:

  • Forensic Science
  • Acoustics
  • Signal Processing

Background:

  • Forensic speaker recognition in Brazil lacks objective methods, relying on subjective analysis and untrustworthy techniques.
  • Current speaker verification uses limited, confrontation-specific samples, requiring excessive data for comparative analysis.
  • Existing systems struggle to identify speakers from pre-selected individuals in contested discourse.

Purpose of the Study:

  • To propose a novel, objective speaker verification tool for forensic analysis in Brazil.
  • To establish a reliable method for determining speaker confidence and similarity in forensic reports.
  • To contribute an accurate, rapid, and alternative approach to speaker verification.

Main Methods:

  • Utilizing a combination of linear predictive coding (LPC) for feature extraction.
  • Employing ordinary least squares (OLS) for statistical comparison and verification.
  • Developing a framework robust to noise and variations in audio quality and content.

Main Results:

  • Achieved a preliminary 100% hit rate on a limited Brazilian Portuguese dataset.
  • The method extracts a greater number of formants crucial for OLS statistical comparisons.
  • Demonstrated robustness against specific noise levels and audio variations.

Conclusions:

  • The proposed LPC and OLS combination offers a viable and accurate speaker verification tool for forensic applications.
  • This method provides a foundation for objective decision-making in forensic speaker recognition.
  • The framework shows promise for improving the reliability of forensic voice analysis.