ARTICLE Kumatani_ASLP_2009/IDIAP Beamforming with a Maximum Negentropy Criterion Kumatani, Kenichi McDonough, John Rauch, Barbara Klakow, Dietrich Garner, Philip N. Li, Weifeng EXTERNAL https://publications.idiap.ch/attachments/papers/2008/Kumatani_ASLP_2009.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/kumatani:rr08-29 Related documents IEEE Transactions on Audio Speech and Language Processing 17 5 994-1008 2009 July 2009 In this paper, we address a beamforming application based on the capture of far-field speech data from a single speaker in a real meeting room. After the position of the speaker is estimated by a speaker tracking system, we construct a subband-domain beamformer in generalized sidelobe canceller (GSC) configuration. In contrast to conventional practice, we then optimize the active weight vectors of the GSC so as to obtain an output signal with maximum negentropy (MN). This implies the beamformer output should be as non-Gaussian as possible. For calculating negentropy, we consider the Γ and the generalized Gaussian (GG) pdfs. After MN beamforming, Zelinski post- filtering is performed to further enhance the speech by remov- ing residual noise. Our beamforming algorithm can suppress noise and reverberation without the signal cancellation problems encountered in the conventional beamforming algorithms. We demonstrate this fact through a set of acoustic simulations. More- over, we show the effectiveness of our proposed technique through a series of far-field automatic speech recognition experiments on the Multi-Channel Wall Street Journal Audio Visual Corpus (MC- WSJ-AV,',','), a corpus of data captured with real far-field sensors, in a realistic acoustic environment, and spoken by real speakers. On the MC-WSJ-AV evaluation data, the delay-and-sum beamformer with post-filtering achieved a word error rate (WER) of 16.5%. MN beamforming with the Γ pdf achieved a 15.8% WER, which was further reduced to 13.2% with the GG pdf, whereas the simple delay-and-sum beamformer provided a WER of 17.8%. To the best of our knowledge, no lower error rates at present have been reported in the literature on this ASR task. REPORT kumatani:rr08-29/IDIAP Adaptive Beamforming with a Maximum Negentropy Criterion Kumatani, Kenichi McDonough, John Rauch, Barbara Garner, Philip N. Li, Weifeng Dines, John EXTERNAL https://publications.idiap.ch/attachments/reports/2008/kumatani-idiap-rr-08-29.pdf PUBLIC Idiap-RR-29-2008 2008 IDIAP This paper presents an adaptive beamforming application based on the capture of far-field speech data from a real single speaker in a real meeting room. After the position of a speaker is estimated by a speaker tracking system, we construct a subband-domain beamformer in generalized sidelobe canceller (GSC) configuration. In contrast to conventional practice, we then optimize the active weight vectors of the GSC so that the distribution of an output signal is as non-Gaussian as possible. We consider kurtosis in order to measure the degree of non-Gaussianity. Our beamforming algorithms can suppress noise and reverberation without the signal cancellation problems encountered in conventional beamforming algorithms. We demonstrate the effectiveness of our proposed techniques through a series of far-field automatic speech recognition experiments on the Multi-Channel Wall Street Journal Audio Visual Corpus (MC-WSJ-AV). The beamforming algorithm proposed here achieved a 13.6\% WER, whereas the simple delay-and-sum beamformer provided a WER of 17.8\%.