A study on the speaker recognition algorithm based on Bayesian compressed sensing theory - Yan Yang; Li Zhao (КНУБА, 2019)
Speaker recognition technology is widely used in the Internet and communication filed. In recent years, compressed sensing theory attracted wide attention in and abroad. Having broken through the limitations of the Nyquist sampling rate, it can sampled compressible signals while compressing them at the same time.As a new theory, compressed sensing theory was brought into the filed of speaker recognition technology where huge breakthrough is in great demand to bring hope to enhance the performance of speaker recognition system. Aiming at the text independent speaker recognition technology, this paper made a profound study on the Bayesian compressed sensing algorithm. According to the characteristics of the sparse coefficient in the algorithm, a Gaussian prior assumption is introduced. And then a speaker recognition algorithm based on Bayesian compressed sensing is proposed.
Keywords: speaker; recognition; compressed sensing; sparse representation; BCS GMM; 004.934.1'1