Predicting Speech Intelligibility Based on Across-Frequency Contrast in Simulated Auditory-Nerve Fluctuations.

The present study proposes a modeling approach for predicting speech intelligibility for normal-hearing (NH) and hearing-impaired (HI) listeners in conditions of stationary and fluctuating interferers. The model combines a non-linear model of the auditory periphery with a decision process that is based on the contrast across characteristic frequency (CF) after modulation analysis in the range of the fundamental frequency of speech. Specifically the short-term across-CF correlation between noisy speech and noise alone is assumed to be inversely related to speech intelligibility.

Assessing the efficacy of hearing-aid amplification using a phoneme test.

Consonant-vowel (CV) perception experiments provide valuable insights into how humans process speech. Here, two CV identification experiments were conducted in a group of hearing-impaired (HI) listeners, using 14 consonants followed by the vowel /ɑ/. The CVs were presented in quiet and with added speech-shaped noise at signal-to-noise ratios of 0, 6, and 12 dB.

Predicting speech intelligibility based on a correlation metric in the envelope power spectrum domain.

Unlabelled: A speech intelligibility prediction model is proposed that combines the auditory processing front end of the multi-resolution speech-based envelope power spectrum model [mr-sEPSM; Jørgensen, Ewert, and Dau (2013). J. Acoust.