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An acoustic front-end for robust automatic speech recognition in noisy and reverberant environments is proposed in this contribution. It comprises a blind source separation-based signal extraction scheme and only requires two microphone signals. The proposed front-end and its integration into the recognition system is analyzed and evaluated in noisy living(More)
—The availability of wireless technologies leads from monaural or bilateral hearing aids to binaural processing strategies. In this paper, we investigate a class of blind source separation (BSS)-based speech enhancement algorithms for binaural hearing aids. The blind binaural processing strategies are analyzed and evaluated for different scenarios, i.e.,(More)
In this paper, we present a detailed analysis for two generic single-channel Wiener filtering concepts for binaural hearing aids, namely, a dual-channel filter approach (individual filters for individual channels) and a single filter approach (one filter applied to both channels). After a general description of the concept, this scheme is thoroughly(More)
We propose a novel approach for estimating a reverberation model for a robust recognizer according to [1], which is designed to allow distant-talking automatic speech recognition (ASR) in reverberant environments. Based on a few calibration utterances with known transcriptions recorded in the target environment, a maximum likelihood estimator is used to(More)
In this paper, minimization of the statistical dependence is exploited for acoustic source localization purposes. Originally developed for the separation of signal mixtures, we show that Independent Component Analysis (ICA) can also be successfully applied to localize multiple simultaneously active sound sources, with possibly less sensors than sources.(More)
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