Related Experiment Video
Updated: May 27, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
Published on: September 27, 2024
Relationship between speech recognition in noise and sparseness.
Guoping Li1, Mark E Lutman, Shouyan Wang
1Institute of Sound and Vibration Research, University of Southampton, Southampton, UK.
Speech recognition in noise can be predicted using kurtosis, a measure of signal sparseness in noisy speech. This new method avoids needing clean speech samples, offering a simpler alternative for predicting intelligibility.
Area of Science:
- Acoustic analysis
- Speech perception
- Signal processing
Background:
- Predicting speech recognition in noise typically requires clean speech, limiting practical applications.
- An alternative approach using characteristics of noisy speech is needed.
Purpose of the Study:
- To evaluate an alternative method for predicting speech recognition in noise.
- To assess the utility of kurtosis, a measure of signal sparseness, in noisy speech.
Main Methods:
- Acoustic analysis of vowel-consonant-vowel syllables in babble noise.
- Comparison of kurtosis, glimpsing areas, and extended speech intelligibility index (ESII) with speech recognition scores.
- Manipulation of kurtosis to study its effect on speech recognition in normal-hearing listeners.
Main Results:
- Kurtosis in noisy speech strongly correlated with established prediction models (glimpsing and ESII).
- Kurtosis, glimpsing, and ESII all effectively predicted speech recognition scores.
- A clear monotonic relationship was observed between speech recognition scores and kurtosis.
Conclusions:
- Speech recognition in noise is closely linked to the sparseness (kurtosis) of the noisy speech signal.
- Kurtosis offers a viable alternative for predicting speech intelligibility without requiring clean speech references.
More Related Videos
06:04Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
Published on: March 24, 2023
05:48Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
Published on: August 9, 2024
Related Concept Videos
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Difference from Background: Limit of Detection
The LOD indicates the presence or absence...
Expected Frequencies in Goodness-of-Fit Tests
Classification of Signals
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Chunking and Rehearsal in Sensory Memory
Determination of Expected Frequency