Emergence of Language-Specific Phoneme Classifiers in Self-Organized Maps

The difference between self-organizing maps based phoneme classifiers that emerge for different input languages is studied. For each such language a self-organizing map is trained on mel-frequency cepstral coefficient (MFCC) converted auditory input to form a phoneme classifier. Unsupervised learning is used as the training method. The emerging classes are then compared to the classes found in the International Phonetic Alphabet. Particular class differences across languages and speakers are discussed. We show that SOMs adapt to speakers and languages, even when only given a small training data-set. Additionally, we show that some neurons in SOMs react only to input in one of the two trained languages and that some neurons can be used as word boundary classifiers.