In this paper, a bottom-up, activation-based paradigm for continuous speech recognition is described. Speech is described by co-occurrence statistics of acoustic events over an analysis window of variable length, leading to a vectorial representation of high but fixed dimension called "Histogram of Acoustic Co-occurrence" (HAC). During training, recurring acoustic patterns are discovered and associated to words through non-negative matrix factorisation. During testing, word activations are computed from the HAC-representation and their time of occurrence is estimated. Hence, words in a continuous utterance can be detected, ordered and located.
Bibliographic reference. Van hamme, Hugo (2008): "HAC-models: a novel approach to continuous speech recognition", In INTERSPEECH-2008, 2554-2557.