A Feature Selection Method in Spectro-Temporal Domain Based on Gaussian Mixture Models

摘要：

Spectro-temporal representation of speech is considered as one of the leading speech representation approaches in speech recognition systems in recent years. This representation is suffered from high dimensionality of the features space which makes this domain unusable in practical speech recognition systems. In this paper, a new method of feature selection is proposed in the spectro-temporal domain. In this method, clustering techniques are applied to spectrotemporal domain to reduce the dimensions of the features space. In the proposed approach, spectro-temporal space is clustered based on Gaussian Mixture Models (GMMs). The mean vectors and covariance matrices elements of the clusters are considered as a part of the feature vector of the frame. The tests were conducted for new feature vectors on voiced stops (/b/, /d/, /g/) classification of the TIMIT database. Using the new feature vectors, the results were improved to 70.45％ which is 7.95％ higher than last best results.

关键词： component Speech recognition Speech processing auditory system Feature extraction Clustering methods

作者: Nafiseh Esfandian Farbod Razzazi Alireza Behra Sara Valipour

作者单位: Faculty of Engineering, Islamic Azad University, Qaemshahr Branch, QaemShahr, Iran Faculty of Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran Faculty of Engineering, Shahed University, Tehran, Iran Faculty of Engineering, Islamic Azad University, Arak Branch, Arak, Iran

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 522-525

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）