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4.1.Weight Analysis

The neurons of a neural network act as pattern classifiers. The inputs selectively activate one or another neuron, depending on the weight vectors. This means that information about network structure may be extracted from the weight vectors.

In this section we will present a cluster analysis of the neurons in the output layer. For that purpose, the mean weight vectors of the output layer of one of the networks - SRN₂^{4 0} (from Table 1) - were clustered using a minimum variance (Ward's) method, and each vector in the resulting dendrogram was labeled with the phoneme it corresponds to.^₂₀ The resulting diagram is shown in Figure 3.

F
igure 3. Cluster analysis of the vector of the output neurons, labeled with the phonemes they correspond to. The weight vectors are split into clusters which roughly correspond to existing phonetic categories.

We can see that the weight vectors (and correspondingly, the phonemes) cluster into some well-known major natural classes - vowels (in the bottom) and consonants (the upper part). The vowels are split into two major categories: low vowels and semi-low, front vowels (/, , a, e/), and high, back ones. The latter, in turn, are clustered into round+ and round- classes. Consonants appear to be categorized in a way less congruent with phonetics. But here, too, some established groups are distinguished. The first subgroup contains non-coronal consonants (/f, k, m, p, x/) with the exceptions of /l/ and /n/. Another subgroup contains voiced obstruents (/, d, , /). The delimiter '#' is also clustered as a consonant, in a group with /t/, which is also natural. The upper part of the figure seems to contain phonemes from different groups, but we can recognize that most of these phonemes are quite rare in Dutch monosyllables, e.g., //, perhaps because they have been 'loaned' from other languages, e.g. /g/.