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Speech enhancement based on truncated and constrained minimum variance estimator (TCMVE) and undecimated wavelet packet non-uniform filterbanks

机译:基于截断和约束的最小方差估计器(TCMVE)和未定义的小波包非均匀滤波器的语音增强

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This paper presents a new method for speech enhancement using a truncated and constrained minimum variance estimator (TCMVE). The constraint allows for a tradeoff between residual noise and speech distortion. Mathematical analysis shows that Ephraim's SDCE and de Moor's MVE can be treated as special cases of TCMVE. Furthermore, SDCE and MVE become equivalent for a particular choice of constraint. TCMVE was applied in multiple bandpass frequency regions using a new undecimated wavelet packet (UDWP) design to approximate a cochlear filterbank. The new algorithm was tested on noisy speech, which was then sent to an automatic speech recognizer (ASR). Both segmental SNR and recognition scores were improved over wavelet denoising for white, pink, and multi-speaker babble noise for many SNRs.
机译:本文使用截断和约束的最小方差估计器(TCMVE)介绍了一种用于语音增强的新方法。约束允许在残留噪声和语音失真之间进行权衡。数学分析表明,以法莲的SDCE和DE MOOR的MVE可以被视为TCMVE的特殊情况。此外,SDCE和MVE对特定的约束选择相当于相同的。使用新的未发送的小波包(UDWP)设计,在多个带通频率区域中应用TCMVE,以近似触摸柱滤波器。在嘈杂的语音上测试了新算法,然后发送到自动语音识别器(ASR)。对于许多SNRS的白色,粉红色和多扬声器禁止噪声,分段SNR和识别分数都改善了对白色,粉红色和多扬声器咔哒声的小波。

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