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

This work addresses the problem of speech separation in the Spanish Languageusing pre-trained deep learning models. As with many speech processing tasks,large databases in other languages different from English are scarce. Thereforethis work explores different training strategies using the Conv-TasNet model asa benchmark. A scale-invariant signal distortion ratio (SI-SDR) metric value of9.9 dB was achieved for the best training strategy. Then, experimentally, weidentified an inverse relationship between the speakers' similarity and themodel's performance, so an improved ConvTasNet architecture was proposed. Theenhanced Conv-TasNet model uses pre-trained speech embeddings to add abetween-speakers cosine similarity term in the cost function, yielding anSI-SDR of 10.6 dB. Lastly, final experiments regarding real-time deploymentshow some drawbacks in the speakers' channel synchronization due to the need toprocess small speech segments where only one of the speakers appears.

An enhanced Conv-TasNet model for speech separation using a speaker distance-based loss function