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Chapter 12 Recognition of Multiple Speech Sources using ICA

In meetings or noisy public places, often a number of speakers are active simultaneously and the sources of interest need to be separated from interfering speech in order to be robustly recognized. Independent component analysis (ICA) has proven to be a valuable tool for this purpose. However, under difficult environmental conditions, ICA outputs may still contain strong residual components of the interfering speakers. In such cases, time-frequency masking can be applied to the ICA outputs to reduce remaining interferences. In order to remain robust against possible resulting artifacts and loss of information, treating the processed speech feature vector as a random variable with time-varying uncertainty, rather than as deterministic, is a helpful strategy. This chapter shows the possibilities of improving recognition of multiple speech signals based on nonlinear postprocessing, applied together with uncertainty-based decoding techniques.