Perceptual Discrimination of Speaking Style Under Cochlear Implant Simulation

Abstract

The findings from the present study demonstrate that perceiving differences in three speaking styles under CI simulation is a difficult task because some important cues to speaking style are not fully available in these conditions. While some cues like speaking rate are available, this information alone may not always be a reliable indicator of a particular speaking style. Some other reliable speaking styles cues, such as degraded acoustic-phonetic information and variability in speaking rate within an utterance, may be available but less salient. However, as in experiment 2, listeners' perception of speaking styles may be modified if they are constrained or trained to use these additional cues, which were more reliable in the context of the present study. Taken together, these results suggest that dealing with speech variability in real-life listening conditions may be a challenge for CI users.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

The impact of speaking style on speech recognition in quiet and multi-talker babble in adult cochlear implant users

“…For each talker, 12 utterances were produced each in the context of a conversation (conversational reduced-"conversational"), from the retelling of a story (retold story-"neutral"), and from a read list (read text-"clear"), for 36 in total. A full description of the acousticphonetic characteristics of a larger set of materials from which the stimuli were selected can be found in the study methods provided in Tamati et al (2019). As summarized in Tamati et al (2019), the clear speech (read text) originating from the larger corpus demonstrated properties consistent with a carefully articulated speaking style: a greater relative number of pauses, a slower speaking rate (although varying across talkers), a higher average F0 and F0 range, and more fully realized sound segments, including more frequent wordfinal [t]-realization, schwa realization in unstressed syllables, word-final [n]-realization, and postvocalic-[r]realization.…”

“…A full description of the acousticphonetic characteristics of a larger set of materials from which the stimuli were selected can be found in the study methods provided in Tamati et al (2019). As summarized in Tamati et al (2019), the clear speech (read text) originating from the larger corpus demonstrated properties consistent with a carefully articulated speaking style: a greater relative number of pauses, a slower speaking rate (although varying across talkers), a higher average F0 and F0 range, and more fully realized sound segments, including more frequent wordfinal [t]-realization, schwa realization in unstressed syllables, word-final [n]-realization, and postvocalic-[r]realization. The characteristics of the clear speech are described in contrast with the conversational speech originating from the larger corpus (conversational reduced), which demonstrated features more consistent with conversational speech: faster speaking rate, a lower average F0 and F0 range, and more frequent reduction/deletion of the four sound segments.…”

“…In real-world listening environments, further signal degradation comes from environmental conditions (e.g., noise, masking speech), and the acousticphonetic variability from across talkers (e.g., gender, age, regional or foreign accent) and within talkers (e.g., speaking style, emotion) (Mattys et al, 2012;Gilbert et al, 2013). Understanding speech in the presence of competing talkers (or "babble"), conversational speech with reduced speech cues (Liu et al, 2004;Tamati et al, 2019), and high talker variability (Faulkner et al, 2015), have all been shown to be challenging for CI users.…”

“…Previous findings imply that CI users would benefit from clear speech in quiet and in the presence of MTB, with a relatively greater benefit for MTB. Alternatively, limitations in CI hearing, associated with a deficit in discriminating speaking style differences (Tamati et al, 2019), may reduce the benefit afforded by a clear speaking style; that is, MTB may actually further limit access to relevant clear speech cues, resulting in a lack of benefit of clear over conversational speech in MTB. Given the vast individual differences attested in CI users (Lazard et al, 2012;Blamey et al, 2013), the second aim of the study was to investigate whether individual differences in speech recognition determine the extent to which CI users are able to benefit from clear speech modifications in quiet and in MTB.…”

High- and Low-Performing Adult Cochlear Implant Users on High-Variability Sentence Recognition: Differences in Auditory Spectral Resolution and Neurocognitive Functioning

“…In principle, high-and low-performing CI users might represent two distinct groups of patients whose outcomes may be more specifically predicted by differences in underlying neurocognitive functioning and auditory skills. These two groups often show large differences in performance in speech recognition tasks: low-performing CI users are more susceptible to noise (e.g., Fu and Nogaki) 15 , as well as other sources of signal degradations, such as speech variability (e.g., Tamati et al) 51 . Furthermore, although both groups perform more poorly under adverse listening conditions, it appears that some high-performing CI users are better able to take advantage of top-down compensatory mechanisms (Bhargava et al) 4 .…”