Abstract
This paper proposes a new training method for the phoneme identification neural network called βneural fuzzy training.β In the proposed training, nondeterministic (fuzzy) class information is assigned to the training signal, in contrast to the traditional method where a deterministic class information is assigned.
This study aims at the realization of a robust neural network, thereby improving the cumulative recognition rate of the phoneme identification and avoiding overtraining. The proposed neural fuzzy training is realized by backpropagation. In the conventional training, a deterministic phoneme class information is assigned to the training signal of the neural network as the value 1 or 0. However, in the proposed training, the fuzzy class information is assigned to the training signal for each training sample as the likelihood value between 0 and 1.
In the proposed training method, the likelihood is calculated by the monotonically decreasing function (such as exp(βΞ± Β· d^2^)) of the distance between the training sample and the closest sample belonging to each phoneme class. The proposed neural fuzzy training method has a problem in that a large amount of computation cost is required since the training signal is determined by calculating the distances to all training samples. To solve this problem, the representative samples in each phoneme class are defined and the likelihood to the phoneme classes are determined by calculating the distance between the representative sample and the training sample.
By this simplification of the likelihood calculation, the computational cost to determine the training signal is reduced considerably. To demonstrate the usefulness of the neural fuzzy training, an experiment is conducted: /b, d, g, m, n, N/ identification, 18 consonant identification and phrase recognition using TDNNβLR. The ATR database is used in the experiment. In the phoneme identification experiment, the speech samples which are extracted using the handβlabel is used. The TDNN is trained using speed samples uttered in word style, and the evaluation is performed using speech samples uttered in phrase style and in sentence style.
In the phrase recognition experiment using TDNNβLR, the TDNN is trained using speed samples uttered word style using a hand label. The evaluation is performed using speech samples uttered in phrase style. In either experiment, an improvement of using the fuzzy training can be observed. Especially, in the phrase recognition experiment using TDNNβLR, the top recognition rate is improved from 71.2 percent to 80.9 percent, and the top 5th recognition rate is improved from 92.8 percent to 96.O percent. Furthermore, it appeared also that the neural fuzzy training is a highβspeed training method.