【摘要】 特征提取是病理嗓音信号检测中至关重要的步骤。针对经验小波变换（Empirical Wavelet Transform,EWT）在处理复杂频谱信号时的频带划分问题，提出一种基于倒谱包络线改进的EWT，自适应地划分元音/a/的第一和第二共振峰频带，通过计算第一和第二共振峰频带内不同帧之间的皮尔逊相关系数，获得EWTPCC(Empirical Wavelet Transform Pearson Correlation Coefficient)特征。实验结果表明，EWTPCC特征结合支持向量机（Support Vector Machines,SVM）的方法，在萨尔布吕肯语料库（Saarbrücken Voice Database,SVD）中的识别率达到87.65%，可以有效地区分正常嗓音与病理嗓音。更多还原

【Abstract】 Feature extraction is a crucial step in the detection of pathological voice signals.To address the issueof frequency band partitioning when dealing with complex spectral signalsin Empirical Wavelet Transform(EWT),an improved EWT based on the cepstral envelope is proposed. It adaptively partitions the first and second resonance peak frequency bands of the vowel/a/,and obtains the EWTPCC(Empirical Wavelet Transform Pearson Correlation Coefficient) feature by calculating the Pearson correlation coefficient between different frames within the first and second resonance peak frequency bands.The experimental results demonstrate that the method combining the EWTPCC feature with Support Vector Machines(SVM) achieves a recognition rate of 87.65% on the Saarbrücken Voice Database(SVD). This approach effectively distinguishes between normal and pathological voices.更多还原