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 Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

United States Courts make audio recordings of oral arguments available aspublic record, but these recordings rarely include speaker annotations. Thispaper addresses the Speech Audio Diarization problem, answering the question of"Who spoke when?" in the domain of judicial oral argument proceedings. Wepresent a workflow for diarizing the speech of judges using audio recordings oforal arguments, a process we call Reference-Dependent Speaker Verification. Weutilize a speech embedding network trained with the Generalized End-to-End Lossto encode speech into d-vectors and a pre-defined reference audio library basedon annotated data. We find that by encoding reference audio for speakers andfull arguments and computing similarity scores we achieve a 13.8% DiarizationError Rate for speakers covered by the reference audio library on a held-outtest set. We evaluate our method on the Supreme Court of the United States oralarguments, accessed through the Oyez Project, and outline future work fordiarizing legal proceedings. A code repository for this research is availableat github.com/JeffT13/rd-diarization

Diarization of Legal Proceedings. Identifying and Transcribing Judicial Speech from Recorded Court Audio