东南大学学报（英文版）2016，Vol.32Issue(4)：402-407,6.DOI:10.3969/j.issn.1003-7985.2016.04.002
用于跨库语音情感识别的时频原子听觉注意模型
Auditory attention model based on Chirplet for cross-corpus speech emotion recognition
摘要
Abstract
To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for feature extraction. First, in order to extract the spectra features, the auditory attention model is employed for variational emotion features detection. Then, the selective attention mechanism model is proposed to extract the salient gist features which show their relation to the expected performance in cross-corpus testing. Furthermore, the Chirplet time-frequency atoms are introduced to the model. By forming a complete atom database, the Chirplet can improve the spectrum feature extraction including the amount of information. Samples from multiple databases have the characteristics of multiple components. Hereby, the Chirplet expands the scale of the feature vector in the time-frequency domain. Experimental results show that, compared to the traditional feature model, the proposed feature extraction approach with the prototypical classifier has significant improvement in cross-corpus speech recognition. In addition, the proposed method has better robustness to the inconsistent sources of the training set and the testing set.关键词
语音情感识别/选择性注意机制/语谱图特征/跨数据库Key words
speech emotion recognition/selective attention mechanism/spectrogram feature/cross-corpus分类
信息技术与安全科学引用本文复制引用
张昕然,宋鹏,查诚,陶华伟,赵力..用于跨库语音情感识别的时频原子听觉注意模型[J].东南大学学报（英文版）,2016,32(4):402-407,6.基金项目
The National Natural Science Foundation of China ( No.61273266,61231002,61301219,61375028), the Specialized Re-search Fund for the Doctoral Program of Higher Education ( No.20110092130004), the Natural Science Foundation of Shandong Prov-ince ( No. ZR2014FQ016) （ No.61273266,61231002,61301219,61375028）
