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Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages
Parkinson's disease (PD) is a neurological disorder impacting a person's speech.
Representation Learning Strategies to Model Pathological Speech: Effect of Multiple Spectral Resolutions
The proposed models are able to classify the speech from Parkinson's disease patients with accuracy up to 95\%.
Self-Supervised Speech Representations Preserve Speech Characteristics while Anonymizing Voices
Collecting speech data is an important step in training speech recognition systems and other speech-based machine learning models.
Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition
Compared to using Fbank features, XLSR-based features reduced WERs by 6. 8%, 22. 0%, and 7. 0% for the UASpeech, PC-GITA, and EasyCall corpus, respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Common Phone: A Multilingual Dataset for Robust Acoustic Modelling
A Wav2Vec 2. 0 acoustic model was trained with the Common Phone to perform phonetic symbol recognition and validate the quality of the generated phonetic annotation.
The Phonetic Footprint of Parkinson's Disease
As one of the most prevalent neurodegenerative disorders, Parkinson's disease (PD) has a significant impact on the fine motor skills of patients.
Classification of Emotions and Evaluation of Customer Satisfaction from Speech in Real World Acoustic Environments
This paper focuses on finding suitable features to robustly recognize emotions and evaluate customer satisfaction from speech in real acoustic scenarios.
Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning
The results also indicate that it is possible to transfer the knowledge from a system trained on a specific type of expressions or idioms such as those typically used in social media into a more formal type of text data, where the amount of data is more scarce and its structure is completely different.
Exploring Facial Expressions and Affective Domains for Parkinson Detection
The principal contributions of this work are: (1) a novel framework to exploit deep face architectures to model hypomimia in PD patients; (2) we experimentally compare PD detection based on single images vs. image sequences while the patients are evoked various face expressions; (3) we explore different domain adaptation techniques to exploit existing models initially trained either for Face Recognition or to detect FAUs for the automatic discrimination between PD patients and healthy subjects; and (4) a new approach to use triplet-loss learning to improve hypomimia modeling and PD detection.
New Spanish speech corpus database for the analysis of people suffering from Parkinson's disease
Different researchers are currently working in the analysis of speech of people with PD, including the study of different dimensions in speech such as phonation, articulation, prosody and intelligibility.
