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

Multi-task learning (MTL) and attention mechanism have been proven toeffectively extract robust acoustic features for various speech-related tasksin noisy environments. In this study, we propose an attention-based MTL (ATM)approach that integrates MTL and the attention-weighting mechanism tosimultaneously realize a multi-model learning structure that performs speechenhancement (SE) and speaker identification (SI). The proposed ATM systemconsists of three parts: SE, SI, and attention-Net (AttNet). The SE part iscomposed of a long-short-term memory (LSTM) model, and a deep neural network(DNN) model is used to develop the SI and AttNet parts. The overall ATM systemfirst extracts the representative features and then enhances the speech signalsin LSTM-SE and specifies speaker identity in DNN-SI. The AttNet computesweights based on DNN-SI to prepare better representative features for LSTM-SE.We tested the proposed ATM system on Taiwan Mandarin hearing in noise testsentences. The evaluation results confirmed that the proposed system caneffectively enhance speech quality and intelligibility of a given noisy input.Moreover, the accuracy of the SI can also be notably improved by using theproposed ATM system.

Attention-based multi-task learning for speech-enhancement and speaker-identification in multi-speaker dialogue scenario