Directly data-derived articulatory gesture-like representations retain discriminatory information about phone categories

Speech perception difficulty modulates theta-band encoding of articulatory synergies

The human brain tracks available speech acoustics and extrapolates missing information such as the speaker's articulatory patterns. However, the extent to which articulatory reconstruction supports speech perception remains unclear. This study explores the relationship between articulatory reconstruction and task difficulty. Participants listened to sentences and performed a speech-rhyming task. Real kinematic data of the speaker's vocal tract were recorded via electromagnetic articulography (EMA) and aligned to corresponding acoustic outputs. We extracted articulatory synergies from the EMA data with principal component analysis (PCA) and employed partial information decomposition (PID) to separate the electroencephalographic (EEG) encoding of acoustic and articulatory features into unique, redundant, and synergistic atoms of information. We median-split sentences into easy (ES) and hard (HS) based on participants' performance and found that greater task difficulty involved greater encoding of unique articulatory information in the theta band. We conclude that fine-grained articulatory reconstruction plays a complementary role in the encoding of speech acoustics, lending further support to the claim that motor processes support speech perception.NEW & NOTEWORTHY Top-down processes originating from the motor system contribute to speech perception through the reconstruction of the speaker's articulatory movement. This study investigates the role of such articulatory simulation under variable task difficulty. We show that more challenging listening tasks lead to increased encoding of articulatory kinematics in the theta band and suggest that, in such situations, fine-grained articulatory reconstruction complements acoustic encoding.

Data-Driven Critical Tract Variable Determination for European Portuguese

Technologies, such as real-time magnetic resonance (RT-MRI), can provide valuable information to evolve our understanding of the static and dynamic aspects of speech by contributing to the determination of which articulators are essential (critical) in producing specific sounds and how (gestures). While a visual analysis and comparison of imaging data or vocal tract profiles can already provide relevant findings, the sheer amount of available data demands and can strongly profit from unsupervised data-driven approaches. Recent work, in this regard, has asserted the possibility of determining critical articulators from RT-MRI data by considering a representation of vocal tract configurations based on landmarks placed on the tongue, lips, and velum, yielding meaningful results for European Portuguese (EP). Advancing this previous work to obtain a characterization of EP sounds grounded on Articulatory Phonology, important to explore critical gestures and advance, for example, articulatory speech synthesis, entails the consideration of a novel set of tract variables. To this end, this article explores critical variable determination considering a vocal tract representation aligned with Articulatory Phonology and the Task Dynamics framework. The overall results, obtained considering data for three EP speakers, show the applicability of this approach and are consistent with existing descriptions of EP sounds.

Variability of articulator positions and formants across nine English vowels

Speech, though communicative, is quite variable both in articulation and acoustics, and it has often been claimed that articulation is more variable. Here we compared variability in articulation and acoustics for 32 speakers in the x-ray microbeam database (XRMB; Westbury, 1994). Variability in tongue, lip and jaw positions for nine English vowels (/u, ʊ, æ, ɑ, ʌ, ɔ, ε, ɪ, i/) was compared to that of the corresponding formant values. The domains were made comparable by creating three-dimensional spaces for each: the first three principal components from an analysis of a 14-dimensional space for articulation, and an F1xF2xF3 space for acoustics. More variability occurred in the articulation than the acoustics for half of the speakers, while the reverse was true for the other half. Individual tokens were further from the articulatory median than the acoustic median for 40-60% of tokens across speakers. A separate analysis of three non-low front vowels (/ε, ɪ, i/, for which the XRMB system provides the most direct articulatory evidence) did not differ from the omnibus analysis. Speakers tended to be either more or less variable consistently across vowels. Across speakers, there was a positive correlation between articulatory and acoustic variability, both for all vowels and for just the three non-low front vowels. Although the XRMB is an incomplete representation of articulation, it nonetheless provides data for direct comparisons between articulatory and acoustic variability that have not been reported previously. The results indicate that articulation is not more variable than acoustics, that speakers had relatively consistent variability across vowels, and that articulatory and acoustic variability were related for the vowels themselves.