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A connected speech recognition method based on the Baum forward backward algorithm is presented. The segmentation of the test sentence uses the probability that an acoustic vector lays at the separation of two speech subunit models (Hidden Markov models). The labelling rests on the highest probability that a vector has been emitted on the last state of a subunit model. Results are presented for word- and phoneme-recognition.