Detecting anticipatory information in speech with signal chopping. (September 2020)

• Record Type: Journal Article
• Authors: Tilsen, Sam
• Is Part Of: Journal of phonetics. Volume 82(2020)
• Publication Date: 2020-09
• Subjects: Speech planning -- Speech production -- Articulation -- Articulatory phonology -- Neural networks -- Deep learning -- Information -- Signal chopping; Phonetics -- Periodicals; Phonetics -- Periodicals; Phonétique -- Périodiques; Phonetics; Periodicals; Electronic journals; 414.05
• DOI: 10.1016/j.wocn.2020.100996
• Languages: English
• ISSNs: 0095-4470

Abstract

Highlights: A new method, signal chopping, is proposed for detecting information in speech signals. Experimental data show that information related to articulatory gestures is present before movement initiation. A model is presented of the mechanisms which give rise to anticipatory information. Abstract: Most analyses of articulatory processes in speech assume that word form-related changes in the state of the vocal tract have well-defined beginnings and ends. But how do we determine the precise moments in time when these beginnings and ends occur? More specifically, when should we expect information related to the sound categories of a word to be present in acoustic and articulatory signals? The framework of Articulatory Phonology/Task Dynamics predicts that the earliest time such information becomes available is when the first articulatory gesture of a word becomes active, which closely corresponds to when a movement is initiated. Alternatively, a recent extension of the Articulatory Phonology model holds that gestures may have an influence on the state of the vocal tract after they have been retrieved from memory, but before they become active and before canonical movement initiation. This paper presents evidence that indeed, anticipatory information is available much earlier than is typically assumed: the identity of a syllable onset gesture can be predicted from articulatory and acoustic data quite early, in some cases nearly half a second before movement initiation. Likewise, the identity of a coda gesture can be predicted during the period of time typically associated with an onset consonant. These findings were obtained with a novel analysis method called signal chopping which was paired with deep neural network based classification. In this approach articulatory and acoustic signals are systematically truncated in space and time, and a network training/test procedure is repeated on the chopped signals. By analyzing the effects of chopping on classification accuracy, gesture-specific information can be spatiotemporally localized.