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

The CTC-based automatic speech recognition (ASR) models without the externallanguage model usually lack the capacity to model conditional dependencies andtextual interactions. In this paper, we present a Gated InterlayerCollaboration (GIC) mechanism to improve the performance of CTC-based models,which introduces textual information into the model and thus relaxes theconditional independence assumption of CTC-based models. Specifically, weconsider the weighted sum of token embeddings as the textual representation foreach position, where the position-specific weights are the softmax probabilitydistribution constructed via inter-layer auxiliary CTC losses. The textualrepresentations are then fused with acoustic features by developing a gateunit. Experiments on AISHELL-1, TEDLIUM2, and AIDATATANG corpora show that theproposed method outperforms several strong baselines.

Improving CTC-based ASR Models with Gated Interlayer Collaboration