Phoneme-retrieval; voice recognition; vowels recognition

A phoneme-retrieval technique is proposed, which is due to the particular way of the construction of the network. An initial set of neurons is given. The number of these neurons is approximately equal to the number of typical structures of the data. For example if the network is built for voice retrieval then the number of neurons must be equal to the number of characteristic phonemes of the alphabet of the language spoken by the social group to which the particular person belongs. Usually this task is very complicated and the network can depend critically on the samples used for the learning. If the network is built for image retrieval then it works only if the data to be retrieved belong to a particular set of images. If the network is built for voice recognition it works only for some particular set of words. A typical example is the words used for the flight of airplanes. For example a command like the "airplane should make a turn of 120 degrees towards the east" can be easily recognized by the network if a suitable learning procedure is used.Comment: 10 page

Learning to segment speech with self-organising maps

In recent years, a number of models of speech segmentation have been developed, including models based on artificial neural networks (ANNs). The latter involved training a recurrent network to predict the next phoneme or utterance boundary, and deriving a means of predicting word boundaries from its behaviour. Here, a different connectionist approach to the task is investigated employing self-organising maps (SOMs) (Kohonen 1990). SOMs differ from other ANNs in that they are unsupervised learners. The aim is to investigate whether the SOM can become sensitive to where word boundaries occur, when trained on phonetically transcribed speech.</p