Previous research has demonstrated techniques to improve automatic speech recognition and speech-in-noise intelligibility for normal hearing (NH) and cochlear implant (CI) listeners by synthesizing Lombard Effect (LE) speech. In this study, we emulate and evaluate segment-specific modifications based on speech production characteristics observed in natural LE speech in order to improve intelligibility for CI listeners. Two speech processing approaches were designed to modify representation of vowels, consonants, and the combination using amplitude-based compression techniques in the "electric domain”–referring to the stimulation sequence delivered to the intracochlear electrode array that corresponds to the acoustic signal. Performance with CI listeners resulted in no significant difference using consonant-boosting and consonant- and vowel-boosting strategies with better representation of mid-frequency and high-frequency content corresponding to both formant and consonant structure, respectively. Spectral smearing and decreased amplitude variation were also observed which may have negatively impacted intelligibility. Segmental perturbations using a weighted logarithmic and sigmoid compression functions in this study demonstrated the ability to improve representation of frequency content but disrupted amplitude-based cues, regardless of comparable speech intelligibility. While there are an infinite number of acoustic domain modifications characterizing LE speech, this study demonstrates a basic framework for emulating segmental differences in the electric domain.
Cite as: Saba, J.N., Hansen, J.H.L. (2022) Speech Modification for Intelligibility in Cochlear Implant Listeners: Individual Effects of Vowel- and Consonant-Boosting. Proc. Interspeech 2022, 5473-5477, doi: 10.21437/Interspeech.2022-11131
@inproceedings{saba22_interspeech,
author={Juliana N. Saba and John H.L. Hansen},
title={{Speech Modification for Intelligibility in Cochlear Implant Listeners: Individual Effects of Vowel- and Consonant-Boosting}},
year=2022,
booktitle={Proc. Interspeech 2022},
pages={5473--5477},
doi={10.21437/Interspeech.2022-11131}
}