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

Punctuated text prediction is crucial for automatic speech recognition as itenhances readability and impacts downstream natural language processing tasks.In streaming scenarios, the ability to predict punctuation in real-time isparticularly desirable but presents a difficult technical challenge. In thiswork, we propose a method for predicting punctuated text from input speechusing a chunk-based Transformer encoder trained with Connectionist TemporalClassification (CTC) loss. The acoustic model trained with long sequences byconcatenating the input and target sequences can learn punctuation marksattached to the end of sentences more effectively. Additionally, by combiningCTC losses on the chunks and utterances, we achieved both the improved F1 scoreof punctuation prediction and Word Error Rate (WER).

Improved Training for End-to-End Streaming Automatic Speech Recognition Model with Punctuation