---
res:
  bibo_abstract:
  - This paper presents an end-to-end training approach for a beamformer-supported
    multi-channel ASR system. A neural network which estimates masks for a statistically
    optimum beamformer is jointly trained with a network for acoustic modeling. To
    update its parameters, we propagate the gradients from the acoustic model all
    the way through feature extraction and the complex valued beamforming operation.
    Besides avoiding a mismatch between the front-end and the back-end, this approach
    also eliminates the need for stereo data, i.e., the parallel availability of clean
    and noisy versions of the signals. Instead, it can be trained with real noisy
    multichannel data only. Also, relying on the signal statistics for beamforming,
    the approach makes no assumptions on the configuration of the microphone array.
    We further observe a performance gain through joint training in terms of word
    error rate in an evaluation of the system on the CHiME 4 dataset.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Jahn
      foaf_name: Heymann, Jahn
      foaf_surname: Heymann
      foaf_workInfoHomepage: http://www.librecat.org/personId=9168
  - foaf_Person:
      foaf_givenName: Lukas
      foaf_name: Drude, Lukas
      foaf_surname: Drude
      foaf_workInfoHomepage: http://www.librecat.org/personId=11213
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Boeddeker, Christoph
      foaf_surname: Boeddeker
      foaf_workInfoHomepage: http://www.librecat.org/personId=40767
  - foaf_Person:
      foaf_givenName: Patrick
      foaf_name: Hanebrink, Patrick
      foaf_surname: Hanebrink
  - foaf_Person:
      foaf_givenName: Reinhold
      foaf_name: Haeb-Umbach, Reinhold
      foaf_surname: Haeb-Umbach
      foaf_workInfoHomepage: http://www.librecat.org/personId=242
  dct_date: 2017^xs_gYear
  dct_language: eng
  dct_title: 'BEAMNET: End-to-End Training of a Beamformer-Supported Multi-Channel
    ASR System@'
...