Automatic speaker identification techniques are widely used nowadays in forensic applications, but its accuracy harshly drops when the voice of the speaker of interest is immersed in a recording containing more than one voice, common situation of investigations where the targets voice are obtained through ambient recordings. In forensic applications where microphones are hidden, such interferent sound sources in recordings are common and they degrade severely the performance of speaker identification techniques. In this paper, we propose a method to mitigate this problem by spatially separating the voice of each speaker using a Blind Source Separation technique called Convolutive Independent Component Analysis, and then applying the separated speech signals to a speaker identification system based on Mel Frequency Cepstral Coefficients and Gaussian Mixture Models. For identifying more than one speaker, the proposed system has a better accuracy than the state-of-the-art solutions.
text-independent, convolutive mixture, blind source separation, independent component analysis, cepstral coefficients, gaussian mixture models.
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THE INTERNATIONAL JOURNAL OF FORENSIC COMPUTER SCIENCE - IJoFCS
Volume 8, Number 1, pages 27-34, DOI: 10.5769/J201301004 or http://dx.doi.org/10.5769/J201301004
Convolutive ICA-Based Forensic Speaker Identification Using Mel Frequency Cepstral Coefficients and Gaussian Mixture Models
By By Matheus A. Silveira, Cezar P. Schroeder, Joăo Paulo C. Lustosa da Costa, Celso G. de Oliveira,
José A. Apolinário Junior, Antonio Manuel Rubio Serrano, Paulo Quintiliano, and Rafael T. de Sousa Júnior
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