基于剩余激励偏度的语音极性自动检测
Residual Excitation Skewness for Automatic Speech Polarity Detection
摘要：
检测正确的语音极性是几种语音处理技术之前的必要步骤。在其确定上的错误可能会对他们的表现产生巨大的不利影响。随着当前系统必须处理来自多个设备的越来越多的数据，语音极性的自动检测已经成为一个至关重要的问题。为此，我们在这里提出了一个非常简单的算法的基础上偏度的两个激励信号。该方法在10个语音语料库（8545个文件）上显示，在干净的条件下，错误率仅为0.06%，并且明显优于四种最先进的方法。此外，它通过其简单性显着降低了计算负荷，并观察到在噪声和混响环境中表现出最强的鲁棒性。
英文摘要：
Detecting the correct speech polarity is a necessary step prior to several speech processing techniques. An error on its determination could have a dramatic detrimental impact on their performance. As current systems have to deal with increasing amounts of data stemming from multiple devices, the automatic detection of speech polarity has become a crucial problem. For this purpose, we here propose a very simple algorithm based on the skewness of two excitation signals. The method is shown on 10 speech corpora (8545 files) to lead to an error rate of only 0.06% in clean conditions and to clearly outperform four state-of-the-art methods. Besides it significantly reduces the computational load through its simplicity and is observed to exhibit the strongest robustness in both noisy and reverberant environments.
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