Modelling asynchrony in speech using elementary single-signal decomposition

Although the possibility of asynchrony between different components of the speech spectrum has been acknowledged, its potential effect on automatic speech recogniser performance has only recently been studied. This paper presents the results of continuous speech recognition experiments in which such asynchrony is accommodated using a variant of HMM decomposition. The paper begins with an investigation of the effects of partitioning the speech spectrum explicitly into subbands. Asynchrony between these sub-bands is then accommodated, resulting in a significant decrease in word errors. The same decomposition technique has previously been used successfully to compensate for asynchrony between the two input streams in an audiovisual speech recognition system.