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/

Global: Zendy Tools annual

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

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We investigate recent transformer networks pre-trained for automatic speechrecognition for their ability to detect speaker and language changes in speech.We do this by simply adding speaker (change) or language targets to the labels.For Wav2vec2 pre-trained networks, we also investigate if the representationfor the speaker change symbol can be conditioned to capture speaker identitycharacteristics. Using a number of constructed data sets we show that thesecapabilities are definitely there, with speaker recognition equal error ratesof the order of 10% and language detection error rates of a few percent. Wewill publish the code for reproducibility.

Speaker and Language Change Detection using Wav2vec2 and Whisper