English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The audio-visual speech fusion strategy AV Align has shown significantperformance improvements in audio-visual speech recognition (AVSR) on thechallenging LRS2 dataset. Performance improvements range between 7% and 30%depending on the noise level when leveraging the visual modality of speech inaddition to the auditory one. This work presents a variant of AV Align wherethe recurrent Long Short-term Memory (LSTM) computation block is replaced bythe more recently proposed Transformer block. We compare the two methods,discussing in greater detail their strengths and weaknesses. We find thatTransformers also learn cross-modal monotonic alignments, but suffer from thesame visual convergence problems as the LSTM model, calling for a deeperinvestigation into the dominant modality problem in machine learning.

Should we hard-code the recurrence concept or learn it instead ? Exploring the Transformer architecture for Audio-Visual Speech Recognition