调信息在汉语语音识别中具有非常重要的意义。采用支持向量机对连续汉语连续语音进行声调识别实

验，首先采用基于Teager能量算子和过零率的两级判别策略对连续语音进行浊音段提取，然后建立了适合于支持向

量机分类模型的等维声调特征向量。使用6个二类SVM模型对非特定人汉语普通话的4种声调进行分类识别，与

BP神经网络相比，支持向量杌具有更高的识别率。-Tone is an essential component for word formation in Chinese languages．It plays a very important role in the

transmission of information in speech communication．We looked at using support vector machines(SVMs)for auto—

matic tone recognition in continuously spoken Mandarin．The voiced segments were detected based on Teager Energy

Operation and ZCIL Compared with BP neural network。considerable improvement was achieved by adopting 6 binary-

SVMs scheme in a speaker-independent Mandarin tone recognition system．