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Found 3 papers, 3 papers with code
Mitigating Catastrophic Forgetting for Few-Shot Spoken Word Classification Through Meta-Learning
We consider the problem of few-shot spoken word classification in a setting where a model is incrementally introduced to new word classes.
Manifold Characteristics That Predict Downstream Task Performance
Pretraining methods are typically compared by evaluating the accuracy of linear classifiers, transfer learning performance, or visually inspecting the representation manifold's (RM) lower-dimensional projections.
Triplet Entropy Loss: Improving The Generalisation of Short Speech Language Identification Systems
Even though the models trained using Triplet Entropy Loss showed a better understanding of the languages and higher accuracies, it appears as though the models still memorise word patterns present in the spectrograms rather than learning the finer nuances of a language.
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