FEATURE VECTOR TRANSFORMATION USING INDEPENDENT COMPONENT ANALYSIS AND ITS APPLICATION TO SPEAKER IDENTIFICATION

This paper presents a feature parameter transformation method using ICA (independent component analysis) for text independent speaker identification of telephone speech. ICA is a signal processing technique which can separate linearly mixed signals into statistically independent signals. The proposed method transforms them into new vectors using ICA assuming that the cepstrum vectors of the telephone speech collected from various kinds of channel conditions are linear combinations of some characteristic functions with random noise added. The performance of the proposed method was compared to the original cepstrum for the HMM-based speaker identification system. Experiments were done in equal and different channel conditions on SPIDRE, a real telephone speech database for text independent speaker identification. The identification rates increased from about 1~13% most cases, so it was confirmed that the proposed method is effective for speaker identification systems, and more effective in adverse environments.