Acoustic to articulatory parameter mapping using an assembly of neural networks

Authors:
M. G. Rahim;W. B. Keijn;J. Schroeter;C. C. Goodyear
Affiliations:
AT& T Bell Labs., Murray Hill, NJ, USA;AT& T Bell Labs., Murray Hill, NJ, USA;AT& T Bell Labs., Murray Hill, NJ, USA;MIT Lincoln Lab., Lexington, MA, USA
Venue:
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Year:
1991

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Abstract

The authors describe an efficient procedure for acoustic-to-articulatory parameter mapping using neural networks. An assembly of multilayer perceptrons, each designated to a specific region in the articulatory space, is used to map acoustic parameters of the speech into tract areas. The training of this model is executed in two stages; in the first stage a codebook of suitably normalized articulatory parameters is used and in the second stage real speech data are used to further improve the mapping. In general, acoustic-to-articulatory parameter mapping is nonunique; several vocal tract shapes can result in identical spectral envelopes. The model accommodates this ambiguity. During synthesis, neural networks are selected by dynamic programming using a criterion that ensures smoothly varying vocal tract shapes while maintaining a good spectral match.