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Found 9 papers, 2 papers with code
Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization
With our strong online data augmentation strategy, the proposed SSReg shows the potential of self-supervised learning without using negative pairs and it can significantly improve the performance of self-supervised speaker representation learning with a simple Siamese network architecture.
Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition
Key challenges in developing generalized automatic emotion recognition systems include scarcity of labeled data and lack of gold-standard references.
Unsupervised Speech Representation Learning for Behavior Modeling using Triplet Enhanced Contextualized Networks
In this paper, we exploit the stationary properties of human behavior within an interaction and present a representation learning method to capture behavioral information from speech in an unsupervised way.
Speaker-invariant Affective Representation Learning via Adversarial Training
In this paper, we propose a machine learning framework to obtain speech emotion representations by limiting the effect of speaker variability in the speech signals.
Linking emotions to behaviors through deep transfer learning
Further, we investigate the importance of emotional-context in the expression of behavior by constraining (or not) the neural networks' contextual view of the data.
Predicting Behavior in Cancer-Afflicted Patient and Spouse Interactions using Speech and Language
Cancer impacts the quality of life of those diagnosed as well as their spouse caregivers, in addition to potentially influencing their day-to-day behaviors.
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context Modeling
In this work we model ASR as a phrase-based noisy transformation channel and propose an error correction system that can learn from the aggregate errors of all the independent modules constituting the ASR and attempt to invert those.
Unsupervised Latent Behavior Manifold Learning from Acoustic Features: audio2behavior
Behavioral annotation using signal processing and machine learning is highly dependent on training data and manual annotations of behavioral labels.
Sparsely Connected and Disjointly Trained Deep Neural Networks for Low Resource Behavioral Annotation: Acoustic Classification in Couples' Therapy
We propose a Sparsely-Connected and Disjointly-Trained DNN (SD-DNN) framework to deal with limited data.
