基于子带矢量量化的鲁棒性话者模型的设计

话音识别系统的时间鲁棒性是影响话者识别系统实用化的关键问题之一。为了提高系统的时间鲁棒性，文章提出了基于子带矢量量化（ＳＢＶＱ）的话者模型。该话者模型由对应语音文本不同频段的各个子带上的矢量量化码本（ＳＢＶＱ码本）构成。ＳＢＶＱ话者模型反映了不同频段对话者识别系统性能的不同影响，并可将时间间隔等因素对系统性能的影响局限在某个子带内从而提高模型的时间鲁棒性。识别时，利用ＢＰ型人工神经网络（ＢＰＮＮ）对训练数据在各个子带上的量化误差进行拟合可进一步提升系统的性能。实验表明，文章提出的ＳＢＶＱ话者模型具有较好的时间鲁棒性。

A method to compress vibration signals using wavelet packet transformation combined with sub-band vector quantization

A novel compression method for mechanical vibrating signals,binding with sub-band vector quantization(SVQ) by wavelet packet transformation(WPT) and discrete cosine transformation(DCT) is proposed.Firstly,the vibrating signal is decomposed into sub-bands by WPT.Then DCT and adaptive bit allocation are done per sub-band and SVQ is performed in each sub-band.It is noted that,after DCT,we only need to code the first components whose numbers are determined by the bits allocated to that sub-band.Through an actual signal,our algorithm is proven to improve the signal-to-noise ratio(SNR) of the reconstructed signal effectively,especially in the situation of lowrate transmission.