EE559: Pattern Recognition Speaker Identification Performance Evaluation

Speaker Identification has been a subject of active research for many years, and has many potential applications where propriety of information is a concern. In this project, a speaker identification system that automatically classifies different speakers based on features extracted from speech waveforms has been implemented. Features used are the power in different frequency subbands of dyadic filterbank, LPC coefficients and power spectral density coefficients. Each of these high-dimensional feature set is reduced to one dimension and the resulting 3-D vector is used as a feature for classification. Speaker Identification has been performed for three cases, one with two different male speakers, with a male and female speaker and with three speakers. The performance of the system is summarized and compared using various parametric and non-parametric techniques.