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Explaining intelligibility in speech-modulated maskers using acoustic glimpse analysis.

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Understanding speech in noise relies on acoustic glimpses. Both the proportion and rate of these glimpses, measured at specific signal-to-noise ratios, are crucial for predicting speech intelligibility in complex listening conditions.

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Area of Science:

  • Auditory perception
  • Speech processing
  • Acoustic analysis

Background:

  • Speech intelligibility in noise is a significant challenge.
  • Acoustic properties of speech signals influence listener perception.
  • Understanding the role of temporal modulation rate (TMR) and masker level is key.

Purpose of the Study:

  • To investigate the relationship between acoustic glimpse metrics and speech intelligibility.
  • To determine how masker level and TMR affect available speech information.
  • To identify optimal acoustic analysis parameters for predicting intelligibility.

Main Methods:

  • Speech intelligibility was measured in speech-modulated noise with varying levels and TMRs.
  • Acoustic analysis quantified speech glimpses above local signal-to-noise ratio criteria (LC).
  • Correlation and regression analyses examined the relationship between glimpse metrics and intelligibility.

Main Results:

  • The proportion and rate of glimpses correlated with intelligibility, influenced by masker level and TMR, respectively.
  • Optimal correlations were achieved at different analysis LCs for each metric.
  • A regression model using both glimpse metrics at -2 dB LC explained 49% of the variance in sentence intelligibility.

Conclusions:

  • Speech recognition in complex maskers is best explained by multidimensional glimpse metrics.
  • Both the proportion and rate of acoustic glimpses are vital for intelligibility.
  • The choice of analysis LC is critical for accurately measuring relevant acoustic information.