Vector Quantization for Speech Compression by a New Version of PRSOM

Abstract

The self-organizing map (SOM) is a popular neural network which was designed for solving problems that involve tasks such as clustering and visualization. Especially, it provides a new strategy of clustering using a competition and co-operation principal. The probabilistic Kohonen network (PRSOM) is the stochastic version of classical one. However, determination of the optimal number of neurons, their initial weights vector and their deviation matrix is still a big problem in the literature. These parameters have a great impact on the learning process of the network, the convergence and the quality of results. Also determination of clusters’ number is a very difficult task. In this paper we propose a new method, called H-PRSOM, which looks for the optimal architecture of the map and determines the suitable codebook for speech compression. According to his hierarchical process, H-PRSOM identifies automatically, in each iteration, new initial parameters of the map. The generated parameters will be used in the learning phase of the probabilistic network. Due to its important propriety of initialization and optimization, we expect that the use of this new version of PRSOM algorithm in the vector quantization might provide good results. In order to evaluate its performance, H-PRSOM model is applied to the problem of speech compression of Arabic digits. The conducted experiments show that the proposed method is able to realize the expected goals.