Modeling Text Independent Speaker Identification with Vector Quantization
Abstract: Speaker
identification is one of the most important technologies 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.
Keywords: speaker
identification, text independent, vector quantization, Indonesian speaker,
K-Means clustering
Author: Syeiva Nurul Desylvia
Journal Code: jptkomputergg170156
