@inproceedings{weber:2000:icslp,
  author = {K. Weber and S. Bengio and H. Bourlard}, 
  title =  {{HMM2}- A novel approach to {HMM} emission probability estimation}, 
  booktitle = {Proceedings of the International Conference on Speech and Language Processing, {ICSLP}},
  year =   2000,
  address = {Beijing, China},
  month = {October},
  url = {publications/ps/rr00-30.ps.gz},
  pdf = {publications/pdf/rr00-30.pdf},
  djvu = {publications/djvu/rr00-30.djvu},
  original={ps/rr00-30.ps.gz},
  topics = {speech},
  abstract = {In this paper, we discuss and investigate a new method to estimate local emission probabilities in the framework of hidden Markov models (HMM). Each feature vector is considered to be a sequence and is supposed to be modeled by yet another HMM. Therefore, we call this approach `HMM2'. There is a variety of possible topologies of such HMM2 systems, e.g. incorporating trellis or ergodic HMM structures. Preliminary HMM2 speech recognition experiments on cepstral and spectral features yielded worse results than state-of-the-art systems. However, we believe that HMM2 systems have a lot of potential advantages and are therefore worth investigating further.},
  categorie = {C}
}

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