A Neural Network Architecture for Learning Word-Referent Associations in Multiple Contexts
Table 2: Correspondence between IPA and ARPAbet symbols and the respective numeric representation of each phoneme.
['Phoneme father', 'IPA', 'ARPAbet AA', 'Numeric Representation 1', 'Numeric Representation 0.5', 'Numeric Representation 1', 'Numeric Representation -1', 'Numeric Representation 0', 'Numeric Representation 0', 'Numeric Representation 0', 'Numeric Representation 0', 'Numeric Representation 0', 'Numeric Representation 0', 'Numeric Representation 0', 'Numeric Representation 0']
Afterward, each phoneme is translated into a vector of 12 real values ranging from -1 to +1 (see This numeric representation was built considering the place of pronunciation of each phoneme in the International Phonetic Alphabet (IPA) charts for vowels and consonants, encoding specific features for vowels (4 of them) and for consonants (8 of them). Therefore, when a vowel is represented, the features for consonants are set to zero, and when a consonant is represented, the features of vowels are set to zero. The rationale behind this procedure is to obtain similar representations for phonemes with similar sounds. The numeric representation of the auditory data was constructed following a procedure similar to the one described in Araujo et al. Then, each phoneme is translated into a vector of 12 real values ranging from -1 to +1, according to its place of pronunciation in the International Phonetic Alphabet (IPA) charts for vowels (4 features) and consonants (8 features). For example, the word “ball” is converted as follow: