We propose a Multigranular Automatic Speech Recognizer. The hypothesis is that
speech signal contains information distributed on more different time scales.
Many works from various scientific fields ranging from neurobiology to speech
technologies, seem to concord on this assumption. In a broad sense, it seems
that speech recognition in human is optimal because of a partial
parallelization process according to which the left-to-right stream of
speech is captured in a multilevel grid in which several linguistic analyses take
place contemporarily. Our investigation aims, in this view, to apply these new
ideas to the project of more robust and efficient recognizers