Robust Speaker Identification using Independent Component Analysis
Gil‐Jin Jang, Yung‐Hwan Oh · Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong · 2000
This paper proposes feature parameter transformation method using independent component analysis (ICA) for speaker identification. The proposed method assumes that the cepstral vectors from various channel-conditioned speech are constructed by a linear combination of some characteristic functions with random channel noise added, and transforms them into new vectors using ICA. The resultant vector space can give emphasis to the repetitive speaker information and suppress the random channel distortions. Experimental results show that the transformation method is effective for the improvement of speaker identification system.