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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 A Comparison of Acoustic and Linguistics Methodologies for Alzheimer's Dementia Recognition Cummins, Nicholas; Pan, Yilin; Ren, Zhao... In: http://infoscience.epfl.ch/record/284990 (2021) BASE Show details 2 Utterance Verification-Based Dysarthric Speech Intelligibility Assessment Using Phonetic Posterior Features Fritsch, Julian; Magimai-Doss, Mathew In: http://infoscience.epfl.ch/record/284148 (2021) BASE Show details 3 Estimating the Degree of Sleepiness by Integrating Articulatory Feature Knowledge in Raw Waveform Based CNNS ... Fritsch, Julian; S. Pavankumar Dubagunta; Magimai.-Doss, Mathew. - : Zenodo, 2020 BASE Show details 4 Estimating the Degree of Sleepiness by Integrating Articulatory Feature Knowledge in Raw Waveform Based CNNS ... Fritsch, Julian; S. Pavankumar Dubagunta; Magimai.-Doss, Mathew. - : Zenodo, 2020 Abstract: Speech-based degree of sleepiness estimation is an emerging research problem. This paper investigates an end-to-end approach, where given raw waveform as input, a convolutional neural network (CNN) estimates at its output the degree of sleepiness. Within this approach, we investigate constraining the first layer processing and integration of speech production knowledge through transfer learning. We evaluate these methods on the continuous sleepiness corpus of the Interspeech 2019 Computational Paralinguistics (ComParE) Challenge and demonstrate that the proposed approach consistently yields competitive systems. In particular, we observe that integration of speech production knowledge aids in improving the performance and yields systems that are complementary. ... Keyword: Paralinguistic speech processing, sleepiness,end-to-end acoustic modeling, convolutional neural networks, artic-ulatory features URL: https://dx.doi.org/10.5281/zenodo.4055894 https://zenodo.org/record/4055894 BASE Hide details 5 Estimating The Degree of Sleepiness by Integrating Articulatory Feature Knowledge In Raw Waveform Based CNNs Fritsch, Julian; Dubagunta, S. Pavankumar; Magimai.-Doss, Mathew In: http://infoscience.epfl.ch/record/276337 (2020) BASE Show details 6 A comparison of acoustic and linguistics methodologies for Alzheimer's dementia recognition Cummins, Nicholas; Pan, Yilin; Ren, Zhao. - 2020 BASE Show details

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