分级语音识别研究

分级识别的策略在模式识别领域中提出相当长的时间了。尽管人类可以训练地使用这个策略进行识别,但对语音识别而言,缺少一个有效的系统化的方法来实现它。本文给出了我们最近在这方面做的一些研究工作,使用了子空间划分原理来实现一个分级识别器,并用树型结构来组织多个识别器。实验结果表明,该方法与传统方法相比,误识率降低10%。我们将在未来的研究工作中,测试全部汉语音节,并将该方法扩展到连续语音识别。

Study on Hierarchical Speech Recognition

Hierarchical recognition has been proposed for a long time in the pattern recognition field. Although it is a familiar action when human performs a recognition task, there is not an effective and systematic method to implement it for the speech recognition. This paper presents our recent experimental results on this topic, which uses the principle of sub-space partition to realize a hierarchical recogntion and a tree-based architecture to organize multi-recognizers. The results show that the proposed algorithm can achieve about 10% error reduction compared with traditional methods. In future works, we will test all Chinese syllables and extend them for the continous speech recogntion.