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Robust speech recognition in a high interference real room environment using blind speech extraction

We present a novel blind signal extraction (BSE) method for robust speech recognition in a real room environment under the coexistence of simultaneous interfering non-speech sources. The proposed method is capable of extracting the target speaker's voice based on a maximum kurtosis criterion. Extensive phoneme recognition experiments have proved the proposed network's efficacy when used in a real-life situation of a talking speaker with the coexistence of various non-speech sources (e.g. music and noise), achieving a phoneme recognition improvement of about 23%, especially under high interference. Furthermore, comparison of the proposed network to known blind source separation networks, commonly used in similar situations, showed lower computational complexity and better recognition accuracy of the BSE network, making it ideal to be used as a front-end to existing ASR systems.