Papers with Hidden Markov Models
Can Rumour Stance Alone Predict Veracity? (C18-1)
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| Challenge: | Existing studies of automatic veracity classification of social media rumours have not explored the effectiveness of crowd stance to determine veracity. |
| Approach: | They propose to use stance as an additional feature to those commonly used in earlier studies to model the veracity of a rumour using Hidden Markov Models and collective stance information to model a social media rumor. |
| Outcome: | The proposed models outperform those using crowd stance and tweets’ times as the only features for modelling true and false rumours. |
LIP-RTVE: An Audiovisual Database for Continuous Spanish in the Wild (2022.lrec-1)
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| Challenge: | Speech perception is considered as a purely auditory process, but it is a multi-modal process involving multiple senses. |
| Approach: | They propose to use a semi-automatically annotated audiovisual database to deal with unconstrained natural Spanish. |
| Outcome: | The proposed system can be used to estimate speech recognition systems in the Deep Learning era. |
Comparison of Conventional Hybrid and CTC/Attention Decoders for Continuous Visual Speech Recognition (2024.lrec-main)
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| Challenge: | Recent advances have been achieved in Visual Speech Recognition (VSR) despite the lack of data, there is no clear comparison between different types of decoders for certain languages and tasks. |
| Approach: | They focused on how the conventional DNN-HMM decoder behaves depending on the amount of data used for their estimation. |
| Outcome: | The proposed model improves the CTC/Attention model in data-scarcity scenarios while requiring less training time and fewer parameters. |
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs (2022.naacl-main)
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| Challenge: | Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) are widely used structured models. |
| Approach: | They use tensor rank decomposition to reduce computational complexities for a subset of FGGs subsuming HMMs and PCFGs. |
| Outcome: | The proposed model performs better on HMM modeling and unsupervised PCFG parsing than previous work. |