LOG SPECTRA ENHANCEMENT USING SPEAKER DEPENDENT PRIORS FOR SPEAKER VERIFICATION
Presented by: Ciira wa Maina, Author(s): Ciira wa Maina, John MacLaren Walsh, Drexel University, United States
We present a variational Bayesian algorithm that enhances the log spectra of noisy speech using speaker dependent priors. This algorithm extends prior work by Frey et al. where the Algonquin algorithm was introduced to enhance speech log spectra in order to improve speech recognition in noisy environments. Our work is built on the intuition that speaker dependent priors would work better than priors that attempt to capture global speech properties. Experimental results using the TIMIT data set and the NIST 2004 speaker recognition evaluation (SRE) data are presented to demonstrate the algorithm's performance.