An Efficient Speaker Recognition System for Separating the Single Channel Speech Using Frequency Modulation

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Separating multiple speech signals from single channel sound signal made a huge challenge between researchers. In our paper, we intended a Modulation Frequency technique for Single channel Speech separation. Speech is perceived as having a pitch at the fundamental frequency of vibration of the vocal chords. The combined speech is first decomposed into a two-dimensional time-frequency representation using Short-time Fourier analysis (STFT) with frequency bins. It will pass through a narrow band pass Filter. A cross channel correlation did not use its fundamental frequency directly and while it uses tools to estimate pitch. A frequency bins contain fundamental frequency will correlate with the frequency bins with harmonics and the preset value to be initiated. A standard speaker identification system based on Gaussian Mixture models is of frame basis to identify the interfering speaker. A two parallel signal separation, one based on correlations of modulation frequency and the other based on fundamental frequency alone approaches is obtained. Our proposed method is implemented in MATLAB and verified using various speech signals. The experimental results show that the proposed technique is more efficient to separate the multiple voices from Single Channel Speech Signal.
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Speech Separation; Frequency Modulation; Short-time Fourier Analysis (STFT); Cross Channel Correlation; YIN Algorithm; Autocorrelation Function (ACF)

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