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Found 9 papers, 0 papers with code
Exploratory Evaluation of Speech Content Masking
Most recent speech privacy efforts have focused on anonymizing acoustic speaker attributes but there has not been as much research into protecting information from speech content.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
Pre-processing Blood-Volume-Pulse for In-the-wild Applications
Our method achieves better results for most time features as well as for a subset of the frequency features.
Interpretability by design using computer vision for behavioral sensing in child and adolescent psychiatry
However, coding human behavior is a time-consuming, expensive task, in which reliability can be difficult to achieve and bias is a risk.
Computational behavior recognition in child and adolescent psychiatry: A statistical and machine learning analysis plan
Features from the videos will be extracted and used to compute ratings of behavior, which will be compared to ratings of behavior produced by mental health professionals trained to use a specific behavioral coding manual.
Speech Detection For Child-Clinician Conversations In Danish For Low-Resource In-The-Wild Conditions: A Case Study
Through our work in this paper, we learned that the model with default classification threshold performs worse on children from the patient group.
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.
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.
