基于LVQ的普米语语谱图识别

Primi Language Spectrogram Recognition Based on Learning Vector Quantization

本文使用基于学习向量量化算法实现了对普米语语谱图的识别。该算法首先通过傅立叶变换绘制出每条普米语语料的语谱图;再次,提取每张语谱图的图像灰度共生矩构建特征向量;最后,运用学习向量量化实现对普米语孤立词的分类。基于学习向量量化算法的分类准确率达到了80.0%。

In this paper, we proposed a Primi language speech recognition algorithm based on Learning Vector Quantization (LVQ). Firstly, the algorithm uses the Fourier transform to plot each Primi language pragmatics;secondly, it extracts the gray level cooccurrence moments of each spectrogram to construct feature vectors;and finally, it uses learning vector quantization to realize the classification of Primi isolated words. The classification accuracy based on the Learning Vector Quantization (LVQ) is 80.0%.