Key result
A real-time voice activity detection module using the S-transform and support vector machines achieved 92% accuracy in detecting speech onset and offset from ECoG signals.
Why the study?
Does a real-time voice activity detection module using ECoG signals accurately detect speech onset and offset in a subject with implanted electrodes?
Does a real-time voice activity detection module using ECoG signals accurately detect speech onset and offset in a subject with implanted electrodes?
A real-time voice activity detector using ECoG signals and S-transform with SVM can achieve 92% accuracy, showing potential for automated natural speech BMI systems.
Answers are grounded in this paper and cite their sources.
Supports ECoG-based speech BMI development; leaves open validation in larger patient cohorts.
In this article, we investigated the performance of a real-time voice activity detection module exploiting different time-frequency methods for extracting signal features in a subject with implanted electrocorticographic (ECoG) electrodes. We used ECoG signals recorded while the subject performed a syllable repetition task. The voice activity detection module used, as input, ECoG data streams, on which it performed feature extraction and classification. With this approach we were able to detect voice activity (speech onset and offset) from ECoG signals with high accuracy. The results demonstrate that different time-frequency representations carried complementary information about voice activity, with the S-transform achieving 92% accuracy using the 86 best features and support vector machines as the classifier. The proposed real-time voice activity detector may be used as a part of an automated natural speech BMI system for rehabilitating individuals with communication deficits.
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Kanas et al. (2014) studied Implanted ECoG electrodes (n=1). Real-time voice activity detection module (S-transform and SVM) was evaluated on Voice activity detection accuracy (speech onset and offset). A real-time voice activity detection module using the S-transform and support vector machines achieved 92% accuracy in detecting speech onset and offset from ECoG signals.
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