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Source separation and noise reduction for automatic speech recognition in dynamic acoustic scenarios

Source separation and noise reduction for automatic speech recognition in dynamic acoustic scenarios
动态声学场景中自动语音识别的源分离和降噪
批准号:
316471544
负责人:
Professor Dr.-Ing. Reinhold Häb-Umbach
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Grants (Transfer Project)
财政年份:
2016
资助国家:
德国
项目状态:
已结题
起止时间:
2015-12-31 至 2020-12-31

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项目成果

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中文摘要
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英文摘要
The goal of the project is the development of an automatic speech recognition system to be used for hands-free voice control in a smart home environment. Starting with the multi-channel blind source separation and noise reduction techniques developed in the DFG-funded preceding project, these algorithms will be further developed and optimized with respect to latency and realizability on resource-constrained embedded hardware. The prototyp to be developed shall perform recognition in real-time with low latency and achieve superior recognition rates in acoustic environments typical of smart home applications compared to an existing speech recognition system available at the industrial partner.As an alternative to the above parametric source separation and speech enhancement algorithms, we will also research and develop neural network based solutions. We will carry out a thorough comparison between the two approaches with respect to enhancement/recogntion performance, latency, computational and memory requirements and robustness towards varying acoustic environmental conditions.
期刊论文(2)
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会议论文
DOI: 10.1007/978-3-030-31372-2_17
发表时间: 2019-10
期刊:
影响因子: --
作者: [Jens Heitkaemper;T. Fehér;M. Freitag;Reinhold Häb-Umbach]
通讯作者: Jens Heitkaemper;T. Fehér;M. Freitag;Reinhold Häb-Umbach
Coordination Funds
Sound recognition with limited supervision over sensor networks
  • 批准号:
    318489874
  • 项目类别:
    Research Units
  • 资助金额:
    $0.0万
  • 财政年份:
    2016
  • 负责人:
    Professor Dr.-Ing. Reinhold Häb-Umbach
  • 依托单位:
Bayesian Learning of a Hierarchical Representation of Language from Raw Speech
Bayesian feature enhancement for large vocabulary speech recognition in the presence of noise and reverberation
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