TY - GEN
T1 - Multi-stream articulator model with adaptive reliability measure for audio visual speech recognition
AU - Xie, Lei
AU - Liu, Zhi-Qiang
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2006
Y1 - 2006
N2 - We propose a multi-stream articulator model (MSAM) for audio visual speech recognition (AVSR). This model extends the articulator modelling technique recently used in audio-only speech recognition to audio-visual domain. A multiple-stream structure with a shared articulator layer is used in the model to mimic the speech production process. We also present an adaptive reliability measure (ARM) based on two local dispersion indicators, integrating audio and visual streams with local, temporal reliability. Experiments on the AVCONDIG database shows that our model can achieve comparable recognition performance with the multi-stream hidden Markov model (MSHMM) under various noisy conditions. With the help of the ARM, our model even performs the best at some testing SNRs. © Springer-Verlag Berlin Heidelberg 2006.
AB - We propose a multi-stream articulator model (MSAM) for audio visual speech recognition (AVSR). This model extends the articulator modelling technique recently used in audio-only speech recognition to audio-visual domain. A multiple-stream structure with a shared articulator layer is used in the model to mimic the speech production process. We also present an adaptive reliability measure (ARM) based on two local dispersion indicators, integrating audio and visual streams with local, temporal reliability. Experiments on the AVCONDIG database shows that our model can achieve comparable recognition performance with the multi-stream hidden Markov model (MSHMM) under various noisy conditions. With the help of the ARM, our model even performs the best at some testing SNRs. © Springer-Verlag Berlin Heidelberg 2006.
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U2 - 10.1007/11739685_104
DO - 10.1007/11739685_104
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 3540335846
SN - 9783540335849
VL - 3930 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 994
EP - 1004
BT - Advances in Machine Learning and Cybernetics - 4th International Conference, ICMLC 2005, Revised Selected Papers
PB - Springer Verlag
T2 - International Conference on Machine Learning and Cybernetics, ICMLC 2005
Y2 - 18 August 2005 through 21 August 2005
ER -