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公开(公告)号：US09009039B2
公开(公告)日：2015-04-14
申请号：US12483262
申请日：2009-06-12
CPC分类号： G10L15/063 , G10L15/144 , G10L15/20
摘要： Technologies are described herein for noise adaptive training to achieve robust automatic speech recognition. Through the use of these technologies, a noise adaptive training (NAT) approach may use both clean and corrupted speech for training. The NAT approach may normalize the environmental distortion as part of the model training. A set of underlying “pseudo-clean” model parameters may be estimated directly. This may be done without point estimation of clean speech features as an intermediate step. The pseudo-clean model parameters learned from the NAT technique may be used with a Vector Taylor Series (VTS) adaptation. Such adaptation may support decoding noisy utterances during the operating phase of a automatic voice recognition system.
摘要翻译： 这里描述了用于噪声自适应训练以实现鲁棒自动语音识别的技术。 通过使用这些技术，噪声自适应训练（NAT）方法可以使用干净和损坏的语音进行训练。 NAT方法可以将环境变形归一化，作为模型训练的一部分。 可以直接估计一组潜在的“伪清理”模型参数。 这可以在没有将干净的语音特征的点估计作为中间步骤的情况下完成。 从NAT技术学习的伪清理模型参数可以与矢量泰勒级数（VTS）适配一起使用。 这种适配可以支持在自动语音识别系统的操作阶段期间解码噪声话语。
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公开(公告)号：US20100318354A1
公开(公告)日：2010-12-16
申请号：US12483262
申请日：2009-06-12
CPC分类号： G10L15/063 , G10L15/144 , G10L15/20
摘要： Technologies are described herein for noise adaptive training to achieve robust automatic speech recognition. Through the use of these technologies, a noise adaptive training (NAT) approach may use both clean and corrupted speech for training. The NAT approach may normalize the environmental distortion as part of the model training. A set of underlying “pseudo-clean” model parameters may be estimated directly. This may be done without point estimation of clean speech features as an intermediate step. The pseudo-clean model parameters learned from the NAT technique may be used with a Vector Taylor Series (VTS) adaptation. Such adaptation may support decoding noisy utterances during the operating phase of a automatic voice recognition system.
摘要翻译： 这里描述了用于噪声自适应训练以实现鲁棒自动语音识别的技术。 通过使用这些技术，噪声自适应训练（NAT）方法可以使用干净和损坏的语音进行训练。 NAT方法可以将环境变形归一化，作为模型训练的一部分。 可以直接估计一组潜在的“伪清理”模型参数。 这可以在没有将干净的语音特征的点估计作为中间步骤的情况下完成。 从NAT技术学习的伪清理模型参数可以与矢量泰勒级数（VTS）适配一起使用。 这种适配可以支持在自动语音识别系统的操作阶段期间解码噪声话语。
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