%Aigaion2 BibTeX export from Idiap Publications
%Tuesday 18 June 2024 10:36:59 AM

@INPROCEEDINGS{stephenson00c,
         author = {Stephenson, Todd Andrew and Bourlard, Herv{\'{e}} and Bengio, Samy and Morris, Andrew},
       projects = {Idiap},
          month = {10},
          title = {Automatic Speech Recognition using Dynamic {B}ayesian Networks with both Acoustic and Articulatory Variables},
      booktitle = {6th International Conference on Spoken Language Processing: ICSLP~2000 (Interspeech~2000)},
           year = {2000},
        address = {Beijing},
           note = {IDIAP-RR 00-19},
       crossref = {stephenson00b},
       abstract = {Current technology for automatic speech recognition (ASR) uses hidden Markov models (HMMs) that recognize spoken speech using the acoustic signal. However, no use is made of the causes of the acoustic signal: the articulators. We present here a dynamic Bayesian network (DBN) model that utilizes an additional variable for representing the state of the articulators. A particular strength of the system is that, while it uses measured articulatory data during its training, it does not need to know these values during recognition. As Bayesian networks are not used often in the speech community, we give an introduction to them. After describing how they can be used in ASR, we present a system to do isolated word recognition using articulatory information. Recognition results are given, showing that a system with both acoustics and inferred articulatory positions performs better than a system with only acoustics.},
            pdf = {https://publications.idiap.ch/attachments/papers/2000/todd-icslp2000.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/papers/2000/todd-icslp2000.ps.gz},
ipdmembership={speech},
}



crossreferenced publications: 
@TECHREPORT{stephenson00b,
         author = {Stephenson, Todd Andrew and Bourlard, Herv{\'{e}} and Bengio, Samy and Morris, Andrew},
       projects = {Idiap},
          title = {Automatic Speech Recognition using Dynamic {B}ayesian Networks with both Acoustic and Articulatory Variables},
           type = {Idiap-RR},
         number = {Idiap-RR-19-2000},
           year = {2000},
    institution = {IDIAP},
           note = {In ``6th International Conference on Spoken Language Processing: ICSLP~2000 (Interspeech~2000)'', 2000},
       abstract = {Current technology for automatic speech recognition (ASR) uses hidden Markov models (HMMs) that recognize spoken speech using the acoustic signal. However, no use is made of the causes of the acoustic signal: the articulators. We present here a dynamic Bayesian network (DBN) model that utilizes an additional variable for representing the state of the articulators. A particular strength of the system is that, while it uses measured articulatory data during its training, it does not need to know these values during recognition. As Bayesian networks are not used often in the speech community, we give an introduction to them. After describing how they can be used in ASR, we present a system to do isolated word recognition using articulatory information. Recognition results are given, showing that a system with both acoustics and inferred articulatory positions performs better than a system with only acoustics.},
            pdf = {https://publications.idiap.ch/attachments/reports/2000/rr00-19.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2000/rr00-19.ps.gz},
ipdmembership={speech},
}