Modeling Text Independent Speaker Identification with Vector Quantization

Abstract

Speaker identification is one of the most important technology nowadays. Many fields such as bioinformatics and security are using speaker identification. Also, almost all electronic devices are using this technology too. Based on number of text, speaker identification divided into text dependent and text independent. On many fields, text independent is mostly used because number of text is unlimited. So, text independent is generally more challenging than text dependent. In this research, speaker identification text independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was 59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This research can be developed using optimization method for VQ parameters such as Genetic Algorithm or Particle Swarm Optimization.