Classification of Speech and Associated EEG Responses from Normal-Hearing and Cochlear Implant Talkers Using Support Vector Machines.
Background/Objectives: Speech produced by individuals with hearing loss differs notably from that of normal-hearing (NH) individuals. Although cochlear implants (CIs) provide sufficient auditory input to support speech acquisition and control, there remains considerable variability in speech intelli...
| Publicado en: | Audiology Research Vol. 15; no. 6; pp. 158 - 172 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
MDPI
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190529848&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190529848 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20394349 EGNC jtl: Audiology Research issn: 20394349 maglogo: N pubinfo: dt: Dec2025 vid: 15 iid: 6 pid: 97109 pub: MDPI artinfo: ui: 190529848 190529848 190529848 10.3390/audiolres15060158 190529848 ppf: 158 ppct: 14 formats: tig: atl: Classification of Speech and Associated EEG Responses from Normal-Hearing and Cochlear Implant Talkers Using Support Vector Machines. aug: au: Raghavendra, Shruthi Lee, Sungmin Tan, Chin-Tuan affil: Independent Researcher, Santa Clarita, CA 91350, USA sug: subj: Speech Classification Electroencephalography Methods Cochlear Implant Hearing Physiology Support Vector Machine Utilization Machine Learning Methods Speech Physiology Human Male Female Adolescence Adult Middle Age Aged Hearing Loss, Sensorineural Descriptive Statistics Data Analysis Software Speech Perception Auditory Cortex Signal Processing, Computer Assisted Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Background/Objectives: Speech produced by individuals with hearing loss differs notably from that of normal-hearing (NH) individuals. Although cochlear implants (CIs) provide sufficient auditory input to support speech acquisition and control, there remains considerable variability in speech intelligibility among CI users. As a result, speech produced by CI talkers often exhibits distinct acoustic characteristics compared to that of NH individuals. Methods: Speech data were obtained from eight cochlear-implant (CI) and eight normal-hearing (NH) talkers, while electroencephalogram (EEG) responses were recorded from 11 NH listeners exposed to the same speech stimuli. Support Vector Machine (SVM) classifiers employing 3-fold cross-validation were evaluated using classification accuracy as the performance metric. This study evaluated the efficacy of Support Vector Machine (SVM) algorithms using four kernel functions (Linear, Polynomial, Gaussian, and Radial Basis Function) to classify speech produced by NH and CI talkers. Six acoustic features—Log Energy, Zero-Crossing Rate (ZCR), Pitch, Linear Predictive Coefficients (LPC), Mel-Frequency Cepstral Coefficients (MFCCs), and Perceptual Linear Predictive Cepstral Coefficients (PLP-CC)—were extracted. These same features were also extracted from electroencephalogram (EEG) recordings of NH listeners who were exposed to the speech stimuli. The EEG analysis leveraged the assumption of quasi-stationarity over short time windows. Results: Classification of speech signals using SVMs yielded the highest accuracies of 100% and 94% for the Energy and MFCC features, respectively, using Gaussian and RBF kernels. EEG responses to speech achieved classification accuracies exceeding 70% for ZCR and Pitch features using the same kernels. Other features such as LPC and PLP-CC yielded moderate to low classification performance. Conclusions: The results indicate that both speech-derived and EEG-derived features can effectively differentiate between CI and NH talkers. Among the tested kernels, Gaussian and RBF provided superior performance, particularly when using Energy and MFCC features. These findings support the application of SVMs for multimodal classification in hearing research, with potential applications in improving CI speech processing and auditory rehabilitation. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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