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Block-diagonal covariance joint subspace tying and model compensation for noise robust automatic speech recognition

机译:块对角协方差联合子空间绑定和模型补偿,用于噪声鲁棒的自动语音识别

摘要

Model compression is combined with model compensation. Model compression is needed in embedded ASR to reduce the size and the computational complexity of compressed models. Model-compensation is used to adapt in real-time to changing noise environments. The present invention allows for the design of smaller ASR engines (memory consumption reduced to up to one-sixth) with reduced impact on recognition accuracy and/or robustness to noises.
机译:模型压缩与模型补偿相结合。嵌入式ASR需要模型压缩,以减小压缩模型的大小和计算复杂性。模型补偿用于实时适应不断变化的噪声环境。本发明允许设计较小的ASR引擎(将存储器消耗减少到六分之一),同时减小对识别精度和/或对噪声的鲁棒性的影响。

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