Articulatory Modelling and ASR - Bibliography
Compiled by K. Kirchhoff (Note: by far not exhaustive!)
1. Estimation of Vocal Tract Shapes/Articulatory Trajectories From Acoustic Data
B.S. Atal, J.J. Chang, M.V. Mathews & J.W. Tukey, ``Inversion of
articulatory-to-acoustic transformation in the vocal tract by a
computer-sorting technique'', JASA 63(5), pp. 1535-1555, 1978
C.S. Blackburn & S.J. Young, ``Towards improved speech recognition using a
speech production model'', Proceedings Eurospeech-95, pp. 1623-1626,
Madrid, Spain, 1995
C.S. Blackburn, Articulatory Methods for Speech Production and
Recognition, PhD Thesis, Cambridge University Engineering Department,
1996
S. Dusan and L. Deng, ``Acoustic-to-articulatory inversion using dynamical and
phonological constraints'', Proceedings of the 5th Speech Production
Workshop: models and data, Kloster Seeon, Germany, 2000
S. Dusan & L. Deng, ``Estimation of articulatory parameters from speech
acoustics by Kalman filtering'', Proceedings of CITO Researcher
Retreat, Hamilton, Canada, 1998
S. Dusan & L. Deng, ``Recovering vocal tract shapes from MFCC parameters'',
Proceedings ICSLP-98, Sydeny, Australia, 1998
S. Dusan, Statistical Estimation of Articulatory Trajectories from the
Speech Signals Using Dynamic and Phonological Constraints, University of
Waterloo, Canada, 2000
J. Hogden et al., ``Accurate recovery of articulator positions from acoustics:
new conclusions based on human data'', Journal of the Acoustical Society
of America 100(3),1996, pp. 1819-1834
T. Kobayashi, M. Yagyu & K. Shirai, ``Application of neural networks to
articulatory motion estimation'', Proceedings ICASSP-91,
pp. 489-492
S. Krstulovic, "LPC-based inversion of the DRM articulatory model",
Proceedings Eurospeech-99, Budapest, Hungary,1999
J. Papcun, T.R. Hochberg, T.R. Thomas, F. Larouche, J. Zacks & S. Levy,
``Inferring articulation and recognizing gestures from acoustics with a neural
network trained on X-ray microbeam data'', JSASA 92, pp. 688-700,
1992
M.G. Rahim, W.B. Kleijn, J. Schroeter & C.C. Goodyear, ``Acoustic to
articulatory parameter mapping using an assembly of neural networks'',
Proceedings of ICASSP-91, pp. 485-488, 1991
H.B. Richards, J.S. Mason, M.J. Hunt & J.S. Bridle, ``Deriving articulatory
representations of speech'', Proceedings Eurospeech-95, pp. 761-764,
1995
H.B. Richards, J.S. Mason, M.J. Hunt & J.S. Bridle, ``Deriving articulatory
representations of speech with various excitation modes'', Proceedings
ICSLP-96, pp. 1233-1236, Philadelphia, USA, 1996
H.B. Richards, J.S. Bridle, J.S. Mason & M.J. Hunt, ``Vocal tract shape
trajectory estimation using MLP analysis-by-synthesis'', 1287-1290
M.R. Schroeder, ``Determination of the geometry of the human vocal tract by
acoustic measurements'', JASA 41(2), pp. 1002-1010, 1967
K. Shirai and M. Honda, "Estimation of Articulatory Motion". in Dynamic
Aspects of Speech Production, pp. 279-302, Tokyo University Press,
1976
S. Suzuki, T. Okadome & M. Honda, ``Determination of articulatory positions
from speech acoustics by applying dynamic articulatory constraints'',
Proceedings ICSLP-98, Sydney, Australia, 1998
J. Zacks & T.R. Thomas, ``A new neural network for articulatory speech
recognition and its application to vowel identification'', Computer,
Speech and Language 8, pp. 189-209, 1994
2. Pseudo-Articulatory Classes
A.M. Abdelatty Ali, J. van der Spiegel and Paul Mueller, "An acoustic-phonetic feature-based system for the automatic recognition of fricative consonants",
Proceedings ICASSP-98 , 1998, pp. 961-964
L. Deng & K. Erler, ``Structural design of hidden Markov model speech
recognizer using multivalued phonetic features: Comparison with segmental
speech units'', JASA 92(6), pp. 3058-3066, 1992
L. Deng & D. Sun, ``Speech recognition using atomic speech units constructed
from overlapping articulatory features'', Proceedings Eurospeech-93,
pp. 1635-1638, Berlin, Germany, 1993
L. Deng & D. Sun, ``Phonetic classification and recognition using HMM
representation of overlapping articulatory features for all classes of English
sounds'', Proceedings ICSSP-94, pp. I-45-48, Adelaide, Australia,
1994
L. Deng, G. Ramsay & D. Sun, ``Production models as a structural basis for
automatic speech recognition'', ETRW-96, 1996
E. Eide, J.R. Rohlicek. H. Gish and S. Mitter,"A linguistic feature
representation of the speech waveform", Proceedings ICASSP-93 ,
1993,pp.483-486
K. Elenius and M. Blomberg, "Comparing phoneme and feature based speech
recognition using artificial neural networks", Proceedings ICSLP-92
1992, 1279-1282
K. Erler & G. H. Freeman, ``An HMM-based speech recognizer using overlapping
articulatory features'', JASA 100(4), pp. 2500-2513, 1996
A.V. Hansen, ``Acoustic parameters optimised for recognition of phonetic
features'', Proceedings of Eurospeech-97, pp. 397-400, Rhodes, Greece,
1997
D.J. Iskra & W.H. Edmondson, ``Feature-based approach to speech recognition'',
Proceedings ICSLP-98, Sydey, Australia, 1998
K. Kirchhoff, ``Combining articulatory and acoustic information for speech
recognition in noisy and reverberant environments'',Proceedings ICSLP-98
,Syndney, Australia, 1998
K. Kirchhoff, ``Conversational speech recognition using acoustic and
articulatory input'', Proceedings ICASSP-00 , Istanbul, Turkey,
2000
S. King, T. Stephenson, S. Isard, P. Taylor & A. Strachan, ``Speech
recognition via phonetically featured syllables'', Proceedings
ICSLP-98, Sydney, Australia, 1998
S. King and P. Taylor, "Detection of phonological features in continuous
speech using neural networks", Computer, Speech and Language 14(4),
pp. 333-345, 2000
P. Steingrimsson et al., "From acoustic signal to phonetic features: a dynamically constrained self-organising neural network",
Proceedings of International Congress of Phonetic Sciences,1995
3. Hidden Articulatory Variables
G. Zweig and S. Russell, ``Probabilistic modelling with Bayesian networks for
ASR'', Proceedings ICSLP-98, 1998
G. Zweig, Speech Recognition with Dynamic Bayesian Networks, PhD
thesis, U.C. Berkeley, 1998
M. Richardson, J. Bilmes and C. Diorio, ``Hidden-articulator Markov models:
performance improvements and robustness to noise'', Proceedings of
ICSLP-00, Beijing, China, 2000
M. Richardson, J. Bilmes and C. Diorio, ``Hidden-articulatory Markov models
for Speech Recognition'', Proceedings ICSA Workshop ASR 2000, Paris,
France, 2000
T. Stephenson et al., ``Automatic speech recognition using dynamic Bayesian networks with both acoustic and articulatory variables'', Proceedings ICSLP-00, Beijing, China, 2000
4. Miscellaneous
O. Schmidbauer, F. Casacuberta, M.J. Castro, G. Hegerl, H. Hoge, J.A. Sanchez
& I. Zlokarnik, ``Articulatory representation and speech technology'',
Language and Speech 36, pp. 331-351, 1993
R.C. Rose, J. Schroeter & M.M. Sondhi, ``An investigation of the potential
role of speech production models in automatic speech recognition'',
Proceedings ICSLP-94, pp. 575-578
R.S. McGowan & A. Faber, ``Introduction to papers on speech recognition and perception from an articulatory point of view'', JASA 99(3), pp. 1680-1681, 1996
5. Articulatory Databases
MOCHA data base