Sixth International Conference on Spoken Language Processing
October 16-20, 2000
Hidden-Articulator Markov Models: Performance Improvements and Robustness to Noise
Matt Richardson, Jeff Bilmes, Chris Diorio
University of Washington, USA
A Hidden-Articulator Markov Model (HAMM) is a Hidden
Markov Model (HMM) in which each state represents an articulatory
configuration. Articulatory knowledge, known to be useful
for speech recognition , is represented by specifying a
mapping of phonemes to articulatory configurations; vocal tract
dynamics are represented via transitions between articulatory
In previous work , we extended the articulatory-feature
model introduced by Erler  by using diphone units and a new
technique for model initialization. By comparing it with a purely
random model, we showed that the HAMM can take advantage
of articulatory knowledge.
In this paper, we extend that work in three ways. First, we decrease
the number of parameters, making it comparable in size to
standard HMMs. Second, we evaluate our model in noisy contexts,
verifying that articulatory knowledge can provide benefits
in adverse acoustic conditions. Third, we use a corpus of sideby-
side speech and articulator trajectories to show that the
HAMM can reasonably predict the movement of the articulators.
- L. Deng and D. Sun (1994). "Phonetic Classification and
Recognition Using HMM Representation of Overlapping
Articulatory Features for all classes of English sounds,"
ICASSP, 1994, pp.45-48
- M. Richardson, J. Bilmes, C. Diorio (2000).
"Hidden-Articulator Markov Models for Speech Recognition",
- K. Erler and G.H. Freeman (1996). "An HMM-based
speech recognizer using overlapping articulatory features,"
J. Acoust. Soc. Am. 100, pp.2500-13
Richardson, Matt / Bilmes, Jeff / Diorio, Chris (2000):
"Hidden-articulator Markov models: performance improvements and robustness to noise",
In ICSLP-2000, vol.3, 131-134.