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

Spectral mapping uses a deep neural network (DNN) to map directly from noisyspeech to clean speech. Our previous study found that the performance ofspectral mapping improves greatly when using helpful cues from an acousticmodel trained on clean speech. The mapper network learns to mimic the inputfavored by the spectral classifier and cleans the features accordingly. In thisstudy, we explore two new innovations: we replace a DNN-based spectral mapperwith a residual network that is more attuned to the goal of predicting cleanspeech. We also examine how integrating long term context in the mimiccriterion (via wide-residual biLSTM networks) affects the performance ofspectral mapping compared to DNNs. Our goal is to derive a model that can beused as a preprocessor for any recognition system; the features derived fromour model are passed through the standard Kaldi ASR pipeline and achieve a WERof 9.3%, which is the lowest recorded word error rate for CHiME-2 dataset usingonly feature adaptation.

An Exploration of Mimic Architectures for Residual Network Based Spectral Mapping