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==== Vásquez-Correa, J. C., Rios-Urrego, C. D., Arias-Vergara, T., Schuster, M., Rusz, J., Nöth, E., & Orozco-Arroyave, J. R. (2021). Transfer learning helps to improve the accuracy to classify patients with different speech disorders in different languages. ''Pattern Recognition Letters'', ''150'', 272–279. <nowiki>https://doi.org/10.1016/j.patrec.2021.04.011</nowiki> ==== *Summary: The paper proposes using transfer learning with convolutional neural networks (CNNs) to classify pathological speech from patients with neurodegenerative disorders like Parkinson's disease (PD) and Huntington's disease (HD). Time-frequency representations of voice onset/offset segments are used as input to the CNNs. Two transfer learning scenarios are explored: 1) transferring a model trained on one language to classify patients speaking a different language, and 2) transferring a model trained on one disorder (e.g. PD) to classify patients with a different disorder (e.g. HD). * RQ: Can transfer learning improve the accuracy of CNN models for classifying pathological speech across different languages and disorders? * Hypothesis: Transferring knowledge from a base model trained on one language/disorder to a target model for a different language/disorder can improve classification accuracy when there is limited data for the target task. * Conclusion: The results suggest transfer learning can improve target model accuracy, but only when the base model is sufficiently accurate. Transferring between similar tasks (e.g. different languages) works better than transferring between very different tasks (e.g. different disorders). * Critical observations: ** Accuracies ranged from 70-89% across languages without transfer learning ** Transferring between languages improved accuracy in some cases (e.g. Spanish -> German improved over training on German alone) ** Transferring between very different disorders like PD and HD did not improve over training directly on the target disorder * Relevance: The paper does not directly address low-resource ASR, but instead focuses on pathological speech classification. However, some insights around transfer learning across languages could potentially be adapted to low-resource ASR scenarios.
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