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In this study, a bottleneck feature derived from a DNN and a cepstral domain denoising autoencoder (DAE)-based dereverberation are presented for distant-talking speaker identification, and a combination of these two approaches is proposed. For the DNN-based bottleneck feature, we noted that DNNs can transform the reverberant speech feature to a new feature space with greater discriminative classification ability for distant-talking speaker recognition. Conversely, cepstral domain DAE-based dereverberation tries to suppress the reverberation by mapping the cepstrum of reverberant speech to that of clean speech with the expectation of improving the performance of distant-talking speaker recognition. Since the DNN-based discriminant bottleneck feature and DAE-based dereverberation have a strong complementary nature, the combination of these two methods is expected to be very effective for distant-talking speaker identification. A speaker identification experiment was performed on a distant-talking speech set, with reverberant environments differing from the training environments. In suppressing late reverberation, our method outperformed some state-of-the-art dereverberation approaches such as the multichannel least mean squares (MCLMS). Compared with the MCLMS, we obtained a reduction in relative error rates of 21.4% for the bottleneck feature and 47.0% for the autoencoder feature. Moreover, the combination of likelihoods of the DNN-based bottleneck feature and DAE-based dereverberation further improved the performance.<\/jats:p>","DOI":"10.1186\/s13636-015-0056-7","type":"journal-article","created":{"date-parts":[[2015,5,11]],"date-time":"2015-05-11T10:18:36Z","timestamp":1431339516000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Deep neural network-based bottleneck feature and denoising autoencoder-based dereverberation for distant-talking speaker identification"],"prefix":"10.1186","volume":"2015","author":[{"given":"Zhaofeng","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Longbiao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Atsuhiko","family":"Kai","sequence":"additional","affiliation":[]},{"given":"Takanori","family":"Yamada","sequence":"additional","affiliation":[]},{"given":"Weifeng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Masahiro","family":"Iwahashi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,5,12]]},"reference":[{"issue":"6","key":"56_CR1","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/MSP.2012.2205029","volume":"29","author":"T Yoshioka","year":"2012","unstructured":"T Yoshioka, A Sehr, M Delcroix, K Kinoshita, R Maas, T Nakatani, W Kellermann, Making machines understand us in reverberant rooms: robustness against reverberation for automatic speech recognition. 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