Application of neural networks to articulatory motion estimation

The authors discuss an application of neural networks (NNs) to the problem of estimating the motion of articulatory organs from speech waves. A four-layer feedforward network was successfully applied to the articulatory parameter estimation problem. The evaluation test was performed using the vowel data in 5200 tokens in the ATR word database. Results show that the difference in estimated articulatory parameter values between the conventional model matching method (MM) and NN is only 0.1, which is about 3% of the value range, on average. For a few data, large differences arise between MM and NN, but this is due to misestimation in MM rather than NN. The percentage of misestimates in NN is less than 50% of that for MM. As for calculation time, NN is 10 times faster than MM. Thus, a high-speed and stable articulatory parameter estimation technique can be realized using neural networks.