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Related Experiment Video

Updated: Nov 12, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Speech Intelligibility Prediction using Spectro-Temporal Modulation Analysis.

Amin Edraki1, Wai-Yip Chan1, Jesper Jensen2

  • 1Department of Electrical and Computer Engineering, Queen's University, Kingston, ON K7L 3N6, Canada.

IEEE/ACM Transactions on Audio, Speech, and Language Processing
|March 22, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces wSTMI, a novel speech intelligibility prediction algorithm for normal-hearing listeners. It effectively predicts speech understanding in noisy conditions by analyzing spectro-temporal modulations.

Keywords:
spectro-temporal modulationspeech intelligibilityspeech quality model

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Area of Science:

  • Auditory Neuroscience
  • Signal Processing
  • Speech Perception

Background:

  • Spectro-temporal modulations are crucial for speech sound analysis in the auditory cortex.
  • Human speech comprehension remains robust in challenging acoustic environments.

Purpose of the Study:

  • To propose an intrusive speech intelligibility prediction (SIP) algorithm, wSTMI, for normal-hearing listeners.
  • To leverage spectro-temporal modulation analysis (STMA) for predicting speech intelligibility in degraded conditions.

Main Methods:

  • Utilized spectro-temporal modulation analysis (STMA) on clean and degraded speech signals.
  • Employed a sparse linear model with Lasso regression to combine modulation frequency channel measures.
  • Optimized parameters by selecting the 8 most salient modulation frequency channels.

Main Results:

  • The wSTMI algorithm demonstrated consistent performance across 13 diverse datasets.
  • Evaluated conditions included modulated noise, noise reduction, reverberation, and speech interruption.
  • Compared wSTMI against 10 other existing SIP algorithms, showing superior or comparable results.

Conclusions:

  • The optimized parameters of wSTMI align with human auditory system's modulation transfer functions.
  • The proposed algorithm provides evidence supporting perceptual characteristics of speech intelligibility.
  • wSTMI offers a robust method for speech intelligibility prediction in various acoustic challenges.