Speaker Identification Using Discrete Wavelet Transform
- 1 Karunya University, India
- 2 , India
Copyright: © 2020 Shanthini Pandiaraj and K. R. Shankar Kumar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This study presents an experimental evaluation of Discrete Wavelet Transforms for use in speaker identification. The features are tested using speech data provided by the CHAINS corpus. This system consists of two stages: Feature extraction stage and the identification stage. Parameters are extracted and used in a closed-set text-independent speaker identification task. In this study the signals are pre-processed and features are extracted using discrete wavelet transforms. The energy of the wavelet coefficients are used for training the Gaussian Mixture Model. Daubechies wavelets are used and the speech samples are analyzed using 8 levels of decomposition.
- Speaker Identification
- Discrete Wavelets
- Gaussian Mixture Model