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ROBUST DETECTION OF VOICED SEGMENTS IN SAMPLES OF EVERYDAY CONVERSATIONS USING UNSUPERVISED HMMS.

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Summary
This summary is machine-generated.

This study introduces a robust harmonic model for detecting voiced speech segments in noisy everyday conversations. The novel approach significantly outperforms standard voice detection methods, improving accuracy in challenging acoustic environments.

Keywords:
life logspeech detectionvoice detection

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Area of Science:

  • Speech processing
  • Acoustic signal analysis
  • Machine learning for audio

Background:

  • Ambient recordings present significant noise challenges for voice detection.
  • Existing methods like get-f0 are sensitive to various noise types.
  • Collecting labeled data for noise-specific models is often infeasible.

Purpose of the Study:

  • To develop a robust method for detecting voiced speech segments in noisy everyday conversations.
  • To improve upon the limitations of traditional cross-correlation-based voice detection.
  • To leverage harmonic properties of speech for more accurate detection.

Main Methods:

  • Utilized harmonic models to capture the periodic and harmonic nature of voiced speech.
  • Employed a maximum a posteriori (MAP) criterion for robust parameter estimation.
  • Integrated harmonic model likelihood into an unsupervised Hidden Markov Model (HMM).
  • Addressed harmonic model limitations by exploiting speech's non-stationary properties.

Main Results:

  • The proposed harmonic model-based HMM significantly improves voice detection accuracy.
  • Demonstrated superior performance compared to standard voice detection algorithms.
  • Empirical evaluation on large corpora of everyday speech confirmed effectiveness.

Conclusions:

  • Harmonic models offer a more robust approach to voice detection in noisy conditions.
  • Combining MAP estimation and HMMs with non-stationarity exploitation enhances speech detection.
  • The developed method provides a significant advancement over existing voice detection tools.