基于非线性取值DTW算法的鲁棒性语音识别系统

Robust Speech Recognition System Based on Nonlinear Extraction Dynamic Time Warping

提出了一个在噪声环境下高效的语音识别系统。针对端点检测,提出了基于平滑函数的检测方法,从而提高了利用短时能量算法的检测精度。运行频谱滤波器方法在能量频谱和对数频谱用了两次带通滤波器减少噪声,在对数频谱内用倒谱均值相减的方法去除卷积噪声,从而减少了计算量。对于普通DTW(Dynamic Time Warpin)算法得到某个测试语音与该语音所有的参考语音相似值,应用一个非线性中值滤波器取中间某个值的方法来进行识别,从而提高了DTW算法的识别精度。利用少量参考语音,实现了高于HMM的识别精度同时又减少了训练的花费时间。

In this paper, an efficient robust speech recognition system in noisy environment was proposed. A smooth function is used to short time energy （STE）, which has improved the detection accuracy of STE. The complexity of running spectrum filtering is high, because two band-pass filter are used. Hence, the cepstrum mean subtraction （CMS） was used to reduce the convolution noise in logarithm spectrum, and the calculation is reduced more much. Unlike convemional DTW （Dynamic Time Warping） algorithms, which search for the reference word with minimum distance from the unknown speech waveform, a nonlinear median filter （NMF） was used and the reference word with minimum median distance from the unknown speech waveform was searched for.DTW implementations can be improved substantially. In this approach yields, DTW recognition accuracy is higher than that of the HMM techniques. However, the training is saved.