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1
Self-Supervised Speech Representations Preserve Speech Characteristics while Anonymizing Voices ...
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2
Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition ...
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3
Common Phone: A Multilingual Dataset for Robust Acoustic Modelling ...
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4
Common Phone: A Multilingual Dataset for Robust Acoustic Modelling ...
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5
Common Phone: A Multilingual Dataset for Robust Acoustic Modelling ...
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6
Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning ...
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7
The Phonetic Footprint of Parkinson's Disease ...
Abstract: As one of the most prevalent neurodegenerative disorders, Parkinson's disease (PD) has a significant impact on the fine motor skills of patients. The complex interplay of different articulators during speech production and realization of required muscle tension become increasingly difficult, thus leading to a dysarthric speech. Characteristic patterns such as vowel instability, slurred pronunciation and slow speech can often be observed in the affected individuals and were analyzed in previous studies to determine the presence and progression of PD. In this work, we used a phonetic recognizer trained exclusively on healthy speech data to investigate how PD affected the phonetic footprint of patients. We rediscovered numerous patterns that had been described in previous contributions although our system had never seen any pathological speech previously. Furthermore, we could show that intermediate activations from the neural network could serve as feature vectors encoding information related to the disease ... : https://www.sciencedirect.com/science/article/abs/pii/S0885230821001169 ...
Keyword: Artificial Intelligence cs.AI; Audio and Speech Processing eess.AS; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG
URL: https://arxiv.org/abs/2112.11514
https://dx.doi.org/10.48550/arxiv.2112.11514
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8
From discourse to pathology: Automatic identification of Parkinson’s disease patients via morphological measures across three languages
In: Cortex (2020)
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9
Characterisation of voice quality of Parkinson's disease using differential phonological posterior features
In: http://infoscience.epfl.ch/record/228286 (2017)
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10
NeuroSpeech: An open-source software for Parkinson's speech analysis
In: http://infoscience.epfl.ch/record/230231 (2017)
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11
On the Impact of Non-modal Phonation On Phonological Features
In: http://infoscience.epfl.ch/record/224540 (2017)
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12
Characterisation of voice quality of Parkinson's disease using differential phonological posterior features
In: http://infoscience.epfl.ch/record/229210 (2017)
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13
Degree of Parkinson's Disease Severity Estimation Based on Speech Signal Processing
In: IEEE 39th International Conference on Telecommunications and Signal Processing ; https://hal.inria.fr/hal-01328198 ; IEEE 39th International Conference on Telecommunications and Signal Processing, Jun 2016, Vienna, Austria (2016)
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14
On the impact of non-modal phonation on phonological features
In: http://infoscience.epfl.ch/record/223036 (2016)
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15
Advances in nonlinear speech processing : 6th international conference ; proceedings
Solé-Casals, Jordi; Carson-Berndsen, Julie; Daoudi, Khalid. - Heidelberg [u.a.] : Springer, 2013
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UB Frankfurt Linguistik
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