dc.contributor.authorXiao, Xiong
dc.contributor.authorZhao, Shengkui
dc.contributor.authorNguyen, Duc Hoang Ha
dc.contributor.authorZhong, Xionghu
dc.contributor.authorJones, Douglas L.
dc.contributor.authorChng, Eng Siong
dc.contributor.authorLi, Haizhou
dc.date.accessioned2016-02-03T08:22:27Z
dc.date.available2016-02-03T08:22:27Z
dc.date.issued2016
dc.identifier.citationXiao, X., Zhao, S., Nguyen, D. H. H., Zhong, X., Jones, D. L., Chng, E. S., et al. (2016). Speech dereverberation for enhancement and recognition using dynamic features constrained deep neural networks and feature adaptation. EURASIP Journal on Advances in Signal Processing, 2016, 4-.en_US
dc.identifier.issn1687-6172en_US
dc.identifier.urihttp://hdl.handle.net/10220/39943
dc.description.abstractThis paper investigates deep neural networks (DNN) based on nonlinear feature mapping and statistical linear feature adaptation approaches for reducing reverberation in speech signals. In the nonlinear feature mapping approach, DNN is trained from parallel clean/distorted speech corpus to map reverberant and noisy speech coefficients (such as log magnitude spectrum) to the underlying clean speech coefficients. The constraint imposed by dynamic features (i.e., the time derivatives of the speech coefficients) are used to enhance the smoothness of predicted coefficient trajectories in two ways. One is to obtain the enhanced speech coefficients with a least square estimation from the coefficients and dynamic features predicted by DNN. The other is to incorporate the constraint of dynamic features directly into the DNN training process using a sequential cost function. In the linear feature adaptation approach, a sparse linear transform, called cross transform, is used to transform multiple frames of speech coefficients to a new feature space. The transform is estimated to maximize the likelihood of the transformed coefficients given a model of clean speech coefficients. Unlike the DNN approach, no parallel corpus is used and no assumption on distortion types is made. The two approaches are evaluated on the REVERB Challenge 2014 tasks. Both speech enhancement and automatic speech recognition (ASR) results show that the DNN-based mappings significantly reduce the reverberation in speech and improve both speech quality and ASR performance. For the speech enhancement task, the proposed dynamic feature constraint help to improve cepstral distance, frequency-weighted segmental signal-to-noise ratio (SNR), and log likelihood ratio metrics while moderately degrades the speech-to-reverberation modulation energy ratio. In addition, the cross transform feature adaptation improves the ASR performance significantly for clean-condition trained acoustic models.en_US
dc.format.extent18 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesEURASIP Journal on Advances in Signal Processingen_US
dc.rights© 2016 Xiao et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en_US
dc.subjectBeamformingen_US
dc.subjectDeep neural networks
dc.subjectDynamic features
dc.subjectFeature adaptation
dc.subjectRobust speech recognition
dc.subjectReverberation challenge
dc.subjectSpeech enhancement
dc.titleSpeech dereverberation for enhancement and recognition using dynamic features constrained deep neural networks and feature adaptationen_US
dc.typeJournal Article
dc.contributor.researchTemasek Laboratoriesen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1186/s13634-015-0300-4
dc.description.versionPublished versionen_US


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