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Cross-spectral methods for processing speech.

D J Nelson1

  • 1Department of Defense, Ft. Meade, Maryland 20755, USA. waveland@erols.com

The Journal of the Acoustical Society of America
|January 5, 2002
PubMed
Summary
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We developed new time-frequency methods to analyze complex speech signals, accurately estimating components like formant frequencies and group delays. This approach offers super-resolution for better signal analysis.

Area of Science:

  • Signal Processing
  • Acoustics
  • Speech Analysis

Background:

  • Conventional methods often assume signal stationarity, limiting analysis of dynamic signals like speech.
  • The Short-Time Fourier Transform (STFT) provides time-frequency representation but has limitations in resolving multicomponent signals.

Purpose of the Study:

  • To develop advanced time-frequency methods for analyzing nonstationary, multicomponent Frequency Modulated (FM) signals, particularly speech.
  • To achieve accurate joint estimation of signal components in both time and frequency, overcoming conventional limitations.

Main Methods:

  • Utilizing group delay, instantaneous frequency, and higher-order phase derivatives computed from the STFT.
  • Implementing a re-mapping algorithm based on phase derivatives to accurately represent FM signal components.

Related Experiment Videos

  • Employing cross-spectral products to avoid phase ambiguities during derivative estimation.
  • Main Results:

    • Demonstrated analytical and example-based convergence of the re-mapping algorithm to the FM representation.
    • Successfully applied methods to estimate speech parameters, including transmission channel group delay and speech formant frequencies.
    • Achieved simultaneous resolution of all FM components under a separability condition, offering super-resolution beyond STFT's Heisenberg uncertainty.

    Conclusions:

    • The proposed methods provide a unified approach for accurate time-frequency estimation of speech components.
    • Enables precise estimation of instantaneous formant frequencies, excitation time, and vocal tract/channel group delays.
    • Offers significant improvements in signal analysis accuracy and component resolution for complex FM signals.