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

Inspired by the success of deep neural networks (DNNs) in speech processing,this paper presents Deep Vocoder, a direct end-to-end low bit rate speechcompression method with deep autoencoder (DAE). In Deep Vocoder, DAE is usedfor extracting the latent representing features (LRFs) of speech, which arethen efficiently quantized by an analysis-by-synthesis vector quantization (AbSVQ) method. AbS VQ aims to minimize the perceptual spectral reconstructiondistortion rather than the distortion of LRFs vector itself. Also, a suboptimalcodebook searching technique is proposed to further reduce the computationalcomplexity. Experimental results demonstrate that Deep Vocoder yieldssubstantial improvements in terms of frequency-weighted segmental SNR, STOI andPESQ score when compared to the output of the conventional SQ- or VQ-basedcodec. The yielded PESQ score over the TIMIT corpus is 3.34 and 3.08 for speechcoding at 2400 bit/s and 1200 bit/s, respectively.

Deep Vocoder: Low Bit Rate Compression of Speech with Deep Autoencoder