An information-theoretic approach to understanding interrupted speech

Abstract

Everyday listening conditions seldom present the listener with a completely intact speech signal. Research on understanding interrupted speech spans the last seven decades, yet few efforts have focused on what parts of the speech signal were being interrupted. Kewley-Port, Fogerty, and others explored the importance of consonants versus vowels for understanding speech when either was replaced by noise, but this approach is limited by their inherent differences such as segment duration. The present approach is inspired by Shannon information theory, where information is defined by unpredictability, uncertainty, or change. We developed a metric of biologically relevant spectral change in the speech signal, termed cochlea-scaled entropy (CSE). Sentences with high-CSE intervals (low predictability = high information) replaced by noise were understood more poorly than sentences with an equal number and duration of low-CSE intervals (high predictability = low information) replaced. CSE has been shown to better predict speech intelligibility than various temporal measures and consonant/vowel status. This approach has been validated across wide ranges of acoustic simulations of cochlear implant processing, suggesting that cochlear implant users might also utilize these information-bearing acoustic changes to understand speech. Extensions and future directions for this work will be discussed