ArticleInterspeech2019
Quantifying Cochlear Implant Users' Ability for Speaker Identification using CI Auditory Stimuli.
Article in Interspeech, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed, 1 citations in OpenAlex.
- Using musical pitch interval comparisons to assess cochlear implant frequency-to-place maps.Frontiers in audiology and otology · 2025Article
- Speech Enhancement for Cochlear Implant Recipients using Deep Complex Convolution Transformer with Frequency Transformation.IEEE/ACM transactions on audio, speech, and language processing · 2024Article
- Familiar and unfamiliar speaker recognition assessment and system emulation for cochlear implant users.The Journal of the Acoustical Society of America · 2023Article
- Convolutional Neural Network-based Speech Enhancement for Cochlear Implant Recipients.Interspeech · 2019Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Speaker recognition is a biometric modality that uses underlying speech information to determine the identity of the speaker. Speaker Identification (SID) under noisy conditions is one of the challenging topics in the field of speech processing, specifically when it comes to individuals with cochlear implants (CI). This study analyzes and quantifies the ability of CI-users to perform speaker identification based on direct electric auditory stimuli. CI users employ a limited number of frequency bands (8 ∼ 22) and use electrodes to directly stimulate the Basilar Membrane/Cochlear in order to recognize the speech signal. The sparsity of electric stimulation within the CI frequency range is a prime reason for loss in human speech recognition, as well as SID performance. Therefore, it is assumed that CI-users might be unable to recognize and distinguish a speaker given dependent information such as formant frequencies, pitch etc. which are lost to un-simulated electrodes. To quantify this assumption, the input speech signal is processed using a CI Advanced Combined Encoder (ACE) signal processing strategy to construct the CI auditory electrodogram. The proposed study uses 50 speakers from each of three different databases for training the system using two different classifiers under quiet, and tested under both quiet and noisy conditions. The objective result shows that, the CI users can effectively identify a limited number of speakers. However, their performance decreases when more speakers are added in the system, as well as when noisy conditions are introduced. This information could therefore be used for improving CI-user signal processing techniques to improve human SID.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.