Whisper intelligibility enhancement based on noise robust feature and SVM  

基于噪声鲁棒性特征和SVM的耳语音可懂度增强(英文)

作　　者：周健[1,2] 赵力[1] 梁瑞宇[1] 方贤勇[2] 

机构地区：[1]东南大学水声信号处理教育部重点实验室,南京210096 [2]安徽大学智能计算与信号处理教育部重点实验室,合肥230601

出　　处：《Journal of Southeast University(English Edition)》2012年第3期261-265,共5页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China (No.61231002,61273266,51075068,60872073,60975017, 61003131);the Ph.D.Programs Foundation of the Ministry of Education of China(No.20110092130004);the Science Foundation for Young Talents in the Educational Committee of Anhui Province(No. 2010SQRL018);the 211 Project of Anhui University(No.2009QN027B)

摘　　要：A machine learning based speech enhancement method is proposed to improve the intelligibility of whispered speech. A binary mask estimated by a two-class support vector machine （SVM） classifier is used to synthesize the enhanced whisper. A novel noise robust feature called Gammatone feature cosine coefficients （GFCCs） extracted by an auditory periphery model is derived and used for the binary mask estimation. The intelligibility performance of the proposed method is evaluated and compared with the traditional speech enhancement methods. Objective and subjective evaluation results indicate that the proposed method can effectively improve the intelligibility of whispered speech which is contaminated by noise. Compared with the power subtract algorithm and the log-MMSE algorithm, both of which do not improve the intelligibility in lower signal-to-noise ratio （SNR） environments, the proposed method has good performance in improving the intelligibility of noisy whisper. Additionally, the intelligibility of the enhanced whispered speech using the proposed method also outperforms that of the corresponding unprocessed noisy whispered speech.提出了一种基于机器学习的耳语音可懂度增强方法.该方法利用已经训练好的2类支持向量机来估计一个二元时频掩蔽值,进而合成增强后的耳语音.输入支持向量机的特征向量GFCCs是基于听觉外周模型进行提取的,具有噪声鲁棒特性.在增强仿真实验中,将该算法同传统语音增强算法进行语音可懂度增强性能比较.客观评价和主观听力实验结果均表明,所提出的方法能有效提高含噪耳语音的听觉可懂度;相比谱减法和log-MMSE方法在低信噪比时无法提高语音可懂度,该方法在低信噪比时仍可有效提高含噪耳语音的听觉可懂度.此外,含噪耳语音通过所提出的方法进行增强后,其可懂度比未增强时明显提高.

关 键 词：whispered speech intelligibility enhancement noise robust feature machine learning 

分 类 号：TN912.35[电子电信—通信与信息系统]