@InProceedings{7362669,
  author = {D. Baby and H. {Van hamme}},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Hybrid input spaces for exemplar-based noise robust speech recognition using coupled dictionaries},
  year = {2015},
  pages = {1676-1680},
  abstract = {Exemplar-based feature enhancement successfully exploits a wide temporal signal context. We extend this technique with hybrid input spaces that are chosen for a more effective separation of speech from background noise. This work investigates the use of two different hybrid input spaces which are formed by incorporating the full-resolution and modulation envelope spectral representations with the Mel features. A coupled output dictionary containing Mel exemplars, which are jointly extracted with the hybrid space exemplars, is used to reconstruct the enhanced Mel features for the ASR back-end. When compared to the system which uses Mel features only as input exemplars, these hybrid input spaces are found to yield improved word error rates on the AURORA-2 database especially with unseen noise cases.},
  keywords = {speech recognition;hybrid input spaces;exemplar-based noise robust speech recognition;coupled dictionaries;exemplar-based feature enhancement;temporal signal context;background noise;modulation envelope spectral representations;Mel features;Mel exemplars;ASR back-end;AURORA-2 database;word error rates;Dictionaries;Discrete Fourier transforms;Speech;Noise measurement;Feature extraction;Training data;Modulation;coupled dictionaries;automatic speech recognition;modulation envelope;non-negative matrix factorization},
  doi = {10.1109/EUSIPCO.2015.7362669},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570101195.pdf},
}

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Baby and H. {Van hamme}},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Hybrid input spaces for exemplar-based noise robust speech recognition using coupled dictionaries},\n year = {2015},\n pages = {1676-1680},\n abstract = {Exemplar-based feature enhancement successfully exploits a wide temporal signal context. We extend this technique with hybrid input spaces that are chosen for a more effective separation of speech from background noise. This work investigates the use of two different hybrid input spaces which are formed by incorporating the full-resolution and modulation envelope spectral representations with the Mel features. A coupled output dictionary containing Mel exemplars, which are jointly extracted with the hybrid space exemplars, is used to reconstruct the enhanced Mel features for the ASR back-end. 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