2,372 research outputs found
Negative vaccine voices in Swedish social media
Vaccinations are one of the most significant interventions to public health, but vaccine hesitancy creates concerns for a portion of the population in many countries, including Sweden. Since discussions on vaccine hesitancy are often taken on social networking sites, data from Swedish social media are used to study and quantify the sentiment among the discussants on the vaccination-or-not topic during phases of the COVID-19 pandemic. Out of all the posts analyzed a majority showed a stronger negative sentiment, prevailing throughout the whole of the examined period, with some spikes or jumps due to the occurrence of certain vaccine-related events distinguishable in the results. Sentiment analysis can be a valuable tool to track public opinions regarding the use, efficacy, safety, and importance of vaccination
A Double Joint Bayesian Approach for J-Vector Based Text-dependent Speaker Verification
J-vector has been proved to be very effective in text-dependent speaker
verification with short-duration speech. However, the current state-of-the-art
back-end classifiers, e.g. joint Bayesian model, cannot make full use of such
deep features. In this paper, we generalize the standard joint Bayesian
approach to model the multi-faceted information in the j-vector explicitly and
jointly. In our generalization, the j-vector was modeled as a result derived by
a generative Double Joint Bayesian (DoJoBa) model, which contains several kinds
of latent variables. With DoJoBa, we are able to explicitly build a model that
can combine multiple heterogeneous information from the j-vectors. In
verification step, we calculated the likelihood to describe whether the two
j-vectors having consistent labels or not. On the public RSR2015 data corpus,
the experimental results showed that our approach can achieve 0.02\% EER and
0.02\% EER for impostor wrong and impostor correct cases respectively
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