Joint learning of depression and anxiety severity directly from speech signals
| Autor: | Quintana Aguasca, Eric |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, King's College London, Vallverdú Bayés, Sisco, Cummins, Nicholas |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
Medicina--Informàtica
Deep learning (Machine learning) Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic [Àrees temàtiques de la UPC] multi-task learning deep learning Processament de la parla Speech processing systems Medicine--Data processing health informatics mental health Transfer learning speech processing Aprenentatge profund |
| Popis: | Advances in digital health and phenotyping technologies are crucial to ensureincreased access to high-quality mental health support services and treatment. Speech is uniquely placed in this regard, as no other mobile health signal contains its singular combination of cognitive, neuromuscular and physiological information. It is this complexity which makes speech a suitable marker for different mentalhealth conditions.However, much research exploring links between speech and depression is limited, and co-morbidities with conditions such as anxiety have not been exploited to help improve machine learning models.The purpose of this project is to jointly learn depression and anxietydirectly from speech signals.For this project, speech signals were split into segments that were converted into Mel-spectrograms. Automatic feature extraction was performed using a CNN-LSTM model that can classify into5 severities ofdepression. With transfer learning, this model was then usedas a pre-trained model for other tasks, such as classifying speech signals into different 4 severities of anxiety or improving modelsfor both co-morbiditiesin different languages. Finally, a Multi-Task learning model is used to jointly detect depression and anxiety. Models that use transfer learning to detectanxiety achieve an improvement from 67% to 72% of accuracy, while multi-Task learning models achieve an accuracy of 71% for both co-morbidities, anxiety and depression. The experiments show promising results, discussing the viability of jointly detecting mental health conditions such as depression and anxiety as well as exploiting the viability of using models pre-trained for just one condition, language or task to fine-tune a model for another condition, language or task, demonstrating that co-morbidities can help to improve models for joint learning severities directly from speech signals. |
| Databáze: | OpenAIRE |
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