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Found 7 papers, 1 papers with code
On Crowdsourcing-design with Comparison Category Rating for Evaluating Speech Enhancement Algorithms
The evaluation of such algorithms often relies on reference-based objective metrics that are shown to correlate poorly with human perception.
Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages
Furthermore, to address the lack of activation and valence labels in the transfer datasets, we annotate the signal samples with activation and valence levels corresponding to a dimensional model of emotions, which were then used to evaluate the quality of the embedding over the transfer datasets.
Towards Transferable Speech Emotion Representation: On loss functions for cross-lingual latent representations
We show that while the DAE has the highest classification accuracy among the methods, the semi-supervised VAE has a comparable classification accuracy and a more consistent latent embedding distribution over data sets.
Data Representativity for Machine Learning and AI Systems
We introduce three measurable concepts to help focus the notions and evaluate different data samples.
Towards Interpretable and Transferable Speech Emotion Recognition: Latent Representation Based Analysis of Features, Methods and Corpora
Furthermore, due to the black-box nature of deep learning algorithms, a newer challenge is the lack of interpretation and transparency in the models and the decision making process.
A generalized linear joint trained framework for semi-supervised learning of sparse features
This paper introduces a novel solution for semi-supervised learning of sparse features in the context of generalized linear model estimation: the generalized semi-supervised elastic-net (s2net), which extends the supervised elastic-net method, with a general mathematical formulation that covers, but is not limited to, both regression and classification problems.
Forest Floor Visualizations of Random Forests
The advantages of forest floor over partial dependence plots is that interactions are not masked by averaging.
