Low Cost Speech Detection using Haar-like Filtering for Sensornet

摘要：

Haar-like filtering based speech detection is proposed as a new and very low calculation cost method for sensornet applications.The simple haarlike filters having variable filter width and shift width are trained to learn appropriate filter parameters from the training samples to detect speech.Our method yielded speech/nonspeech classification accuracy of 96.93％ for the input length of 0.1s.Compared with high performance feature extraction method MFCC (Mel-Frequency Cepstrum Coefficient),the proposed haar-like filtering can be approximately 85.77％ efficient in terms of the amount of add and multiply calculations while capable of achieving the error rate of only 3.03％ relative to MFCC.

作者: Jun Nishimura Tadahiro Kuroda

作者单位: Department of Electronics and Electrical Engineering,Keio University,3-14-1,Hiyoshi,Kohoku-ku,Yokohama 223-8522,Japan

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）