In this work a challenging scenario concerning hands-free continuous speech recognition is investigated. A set of experiments was carried out using microphone arrays having different numbers of omnidirectional sensors and that were placed at different angles and distances from the talker. Both real and simulated array signals, obtained by means of the image method, were used. An enhanced input to a recognizer based on Hidden Markov Models was obtained by a time delay compensation module providing a beamformed signal. HMM adaptation was used to improve recognition performance in the various acoustic conditions. 1. INTRODUCTION Hands-free continuous speech recognition represents a challenging scenario: many experimental activities [1, 2, 3, 4, 5, 6] have been recently devoted to the enhancement of the speech signal and to the compensation of the acoustic mismatch between the training and the testing conditions. The experiments described in this work refer to the use of a Continuous De..
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