In this study, the authors discuss unsupervised separation of two speakers from single microphone recording using empirical mode decomposition (EMD) and Hilbert transform (HT) generally known as Hilbert–Huang transform. A two‐stage separation procedure is proposed for single‐channel (SC) speech separation. Initial stage of separation is done using EMD, HT and instantaneous frequencies. EMD decomposes the mixed signal into oscillatory functions known as intrinsic mode functions (IMFs). Suitable IMFs are selected using successive EMD decomposition and HT is applied to extract the instantaneous frequencies. The speech frames are grouped into two speakers using correlation of instantaneous frequencies between mixed signal and selected IMFs. Second‐stage separation is done by further decomposing the estimated speakers into IMFs and finding the instantaneous amplitudes using HT. A ratio of instantaneous amplitudes of mixed speech and stage 1 recovered speech signal is computed for both speakers. Histogram of the ratio obtained can be used to estimate the ideal binary mask for each speaker. These masks are applied to the speech mixture and the underlying speakers are estimated. The proposed method was compared with the existing unsupervised SC source separation algorithms. The results show significant improvement in objective measures.

In this paper, we propose an approach for processing multispeaker speech signals collected simultaneously using a pair of spatially separated microphones in a real room environment. Spatial separation of microphones results in a fixed time-delay of arrival of speech signals from a given speaker at the pair of microphones. These time-delays are estimated by exploiting the impulse-like characteristic of excitation during speech production. The differences in the time-delays for different speakers are used to determine the number of speakers from the mixed multispeaker speech signals. There is difference in the signal levels due to differences in the distances between the speaker and each of the microphones. The differences in the signal levels dictate the values of the mixing parameters. Knowledge of speech production, especially the excitation source characteristics, is used to derive an approximate weight function for locating the regions specific to a given speaker. The scatter plots of the weighted and delay-compensated mixed speech signals are used to estimate the mixing parameters. The proposed method is applied on the data collected in actual laboratory environment for an underdetermined case, where the number of speakers is more than the number of microphones. Enhancement of speech due to a speaker is also examined using the information of the time-delays and the mixing parameters, and is evaluated using objective measures proposed in the literature.

In this letter, we address the issue of determining the number of speakers from multispeaker speech signals collected simultaneously using a pair of spatially separated microphones. The spatial separation of the microphones results in time delay of arrival of speech signals from a given speaker. The differences in the time delays for different speakers are exploited to determine the number of speakers from the multispeaker signals. The key idea is that for a given speaker, the relative spacings of the instants of significant excitation of the vocal tract system remain unchanged in the direct components of the speech signals at the two microphones. The time delays can be estimated from the cross-correlation of the Hilbert envelopes of the linear prediction residuals of the multispeaker signals collected at the two microphones.