Not logged in : Login
(Sponging disallowed)

About: A Conditional Generative Model for Speech Enhancement     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : bibo:AcademicArticle, within Data Space : demo.openlinksw.com associated with source document(s)

AttributesValues
type
seeAlso
sameAs
http://eprints.org/ontology/hasAccepted
http://eprints.org/ontology/hasDocument
dc:hasVersion
Title
  • A Conditional Generative Model for Speech Enhancement
described by
Date
  • 2018-03-13
Creator
status
Publisher
abstract
  • Deep learning based speech enhancement approaches like Deep Neural Networks (DNN) and Long-Short Term Memory (LSTM) have already demonstrated superior results to classical methods. However these methods do not take full advantage of temporal context information. While DNN and LSTM consider temporal context in the noisy source speech, it does not do so for the estimated clean speech. Both DNN and LSTM also have a tendency to over-smooth spectra, which causes the enhanced speech to sound muffled. This paper proposes a novel architecture to address both issues, which we term a conditional generative model (CGM). By adopting an adversarial training scheme applied to a generator of deep dilated convolutional layers, CGM is designed to model the joint and symmetric conditions of both noisy and estimated clean spectra.We evaluate CGM against both DNN and LSTM in terms of Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI) on TIMIT sentences corrupted by ITU-T P.501 and NOISEX-92 noise in a range of matched and mismatched noise conditions. Results show that both the CGM architecture and the adversarial training mechanism lead to better PESQ and STOI in all tested noise conditions. In addition to yielding significant improvements in PESQ and STOI, CGM and adversarial training both mitigate against over-smoothing.
Is Part Of
Subject
list of authors
volume
  • 37
is topic of
is primary topic of
Faceted Search & Find service v1.17_git144 as of Jul 26 2024


Alternative Linked Data Documents: iSPARQL | ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 08.03.3331 as of Aug 25 2024, on Linux (x86_64-ubuntu_noble-linux-glibc2.38-64), Single-Server Edition (378 GB total memory, 48 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software