Speech Stream Detection in Strong Noise based on Linear Prediction
Speech Stream Detection in Strong Noise based on Linear Prediction
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DOI：
10.1109/iciea.2006.257178
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发表时间：
2006-05
期刊：
影响因子：
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通讯作者：
Rubo Zhang;Tian Wu;Xueyao Li;Dong Xu
中科院分区：
文献类型：
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作者：
Rubo Zhang;Tian Wu;Xueyao Li;Dong Xu
语音信号中往往夹杂着大量的噪声，这些噪声严重削弱了语音信号检测算法的性能。提出了一种基于线性预测技术的低信噪比语音信号检测算法。该方法首先利用线性预测残差进行降噪，然后根据LPC的系数统计量来判断是否包含语音信号。增强只对语音分量进行预测残差运算，对语音共振峰的损伤很小。由于语音信号的大部分能量分量存在于300-3000 Hz之间，因此只考虑该区域内的LPC系数，这也减少了噪声的影响。实验表明，该算法对强噪声，尤其是白噪声具有较强的免疫力。最后，将该算法与基于短时能量的方法在各种噪声条件下的性能进行了比较，并用正确分类概率进行了量化。结果表明，在低信噪比的情况下，该算法在白噪声和工厂噪声的情况下具有较好的整体性能
The speech signal is usually mixed with a great deal of noise, and the noise weakens seriously the performance of the algorithms to detect speech signal. This paper presents a robust algorithm for speech signal detection in low SNR based on the linear prediction technology. The proposed approach firstly decreases noise by using linear predication residual, then decides whether speech signal is contained or not according to the coefficients statistics of LPC. The operation to speech component is only taken in prediction residual by enhancement, which produces little impairment to the formant of the speech. As most of the energy components of speech signals exist in the region between 300 Hz and 3000 Hz, only those LPC coefficients in this region are taken into account, which also reduces the influence from noise. Experiments show that it is particularly immunized for the proposed algorithm to the strong noise, especially in the white noise. At last, the performance of the algorithm is compared to the approach based on short-term energy in various noise condition, and quantified using the probability of correct classification. The results show that the proposed algorithm has an overall better performance than the referred approach, such as white noise and factory noise to low SNR