Skip to main content

Spectro-Temporal Modulation Sensitivity Prospectively Predicts Speech-in-Noise Recognition in Cochlear Implant Users

Journal
Ear and hearing (Q1)
Published
19 August 2026
Study design
Unclassified
Evidence level
Level 5, Expert Opinion (CEBM 5)
Authors
Julia Erb, Malte Wöstmann, Jens Kreitewolf, Jan Peter Thomas, Christiane Völter, Jonas Obleser
PMID
42615312
DOI
10.1097/AUD.0000000000001881

Why clinicians should know about it

  • Picked for Otorhinolaryngology (paper of the day, 23 August 2026): Spectro‑temporal modulation predicts CI speech‑in‑noise

Abstract

OBJECTIVES: For cochlear implant (CI) patients, reliable predictors of speech outcome are desirable. In this longitudinal study, we examined the contributions of spectro-temporal sensitivity to successful speech recognition following cochlear implantation. DESIGN: We assessed N = 46 recently implanted adult patients shortly after CI activation (T1) and 6 months later (T2) with an adaptive ripple discrimination paradigm where dynamic ripples varied around the temporal rate of 4 Hz and spectral scale of 0.5 cyc/oct. At time points T1 and T2, we evaluated speech-in-quiet recognition using the Freiburg number and monosyllabic word test. The Oldenburg speech-in-noise test was administered 1 year after implantation (T3) to a subset of N = 36 CI recipients. RESULTS: Shortly after implantation (T1), temporal ripple discrimination thresholds predicted speech-in-noise recognition 1 year later (T3; Pearson's r = 0.51). In a linear model predicting 1-year speech-in-noise outcome, the predictors temporal or spectral thresholds at T1 and age performed better than speech measures such as Freiburg wordrecognition. CONCLUSIONS: A simple spectro-temporal ripple discrimination test is a reliable predictor of speech-in-noise outcome 1 year after cochlear implantation, over and above established clinical speech tests. It offers an efficient method in clinical settings to improve the early prediction of speech outcome.

Abstract as published, via PubMed.

View on PubMedFull text at the publisherOpen in the app

For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.