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A Deep Scattering Spectrum - Deep Siamese Network Pipeline For Unsupervised Acoustic Modeling.pdf

Zeghidour_SVD_2016_Deep_scat_ABnet.ICASSP.pdf
Preview of A Deep Scattering Spectrum - Deep Siamese Network Pipeline For Unsupervised Acoustic Modeling
🔗 Source: lscp.net
📊 Size: 505 KB
📄 Pages: 5 pages
⬇️ Downloads: 41

Summary

Researchers investigated the role of input features in unsupervised acoustic modeling, replacing standard mel-scaled filterbanks with a richer representation derived from an analytic scattering spectrum. Using a Siamese network with lexical side information, they showed a substantial improvement when filterbanks were replaced by scattering features, even though these features yielded similar performance when tested without training. This improvement was observed in both weakly supervised and unsupervised settings, with the scattering spectrum and ABnet combination outperforming supervised systems. The scattering transform was used to extract features, which are stable, invariant to translations, and retain more information than traditional filterbanks. The ABnet, a Siamese neural network, learned a phone representation from pairs of words, using lexical feedback to refine phonetic categories. The results demonstrated that unsupervised and weakly supervised architectures can benefit from richer features than traditional ones.

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Researchers investigated the role of input features in unsupervised acoustic modeling, replacing standard mel-scaled filterbanks with a richer representation...

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  • File Format: PDF
  • File Size: 505 KB
  • Pages: 5
  • Language: EN
  • Total Downloads: 41
  • Last Updated: 3 days ago

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Deep guideDeep tutorialScattering guideScattering tutorialSpectrum guideSpectrum tutorialDeep guideDeep tutorialSiamese guideSiamese tutorialNetwork guideNetwork tutorialPipeline guidePipeline tutorialUnsupervised guideUnsupervised tutorialAcoustic guideAcoustic tutorialModeling guideModeling tutorial

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