A new Italian dataset of parallel acoustic and articulatory data

Speech listening entails neural encoding of invisible articulatory features

“…We here set out to investigate whether articulatory kinematics that is not visually available to the listener still conveys information about the produced speech that goes above and beyond that contained in the speech envelope. We recorded the EEG brain-wide activity while native-language participants were listening to acoustic speech stimuli taken from the Multi-SPeaKing-style Articulatory corpus (MSPKA; Canevari et al 2015 ). This corpus contains simultaneous recordings of audio and kinematic data of the articulatory tract (measured via electromagnetic articulography; see methods) while speakers were pronouncing Italian sentences.…”

“…This result was obtained through the combination of a series of targeted novel approaches. First and foremost, participants listened to a set of acoustic speech stimuli for which synchronized articulatory data was available ( Canevari et al, 2015 ). EMA data is characterized by a spatial and temporal resolution ( Rebernik et al, 2021 ) that is not otherwise achievable by other technologies for speech kinematic analysis (i.e.…”

“…The stimuli were selected from the Multi-SPeaKing-style Articulatory corpus (MSPKA; Canevari et al, 2015 ) which comprises simultaneous recordings of audio and articulatory (lips, jaws and tongue) data of three mother-tongue speakers pronouncing sentences in Italian. Audio was recorded at a sampling rate of 22.05 kHz.…”

“…To investigate this relevant question, we designed an EEG experiment where participants listened to auditorily presented sentences. The sentences were obtained from a publicly available dataset ( Canevari et al, 2015 ) in which acoustic data is synchronized with articulatory data recorded via electromagnetic articulography (EMA). EMA uses miniaturized sensor coils placed on articulators (lips, jaws, tongue) to measure accurate position data with a high sampling frequency during speech production.…”

An organic artificial soma for spatio-temporal pattern recognition via dendritic integration

“…AMORE-CASA-CIAO-FAME-FELICE-GRAZIE-TRISTE) were collected in the framework of the present study, while the remaining seven (i.e. GIORNATA-INFANZIA-INVENTATO-MASCHERA-ONDOSO-PRIVILEGIO-TIMONE) were extracted from the Multi-SPeaKing-style Articulatory corpus (MSPKA)[29], to rule out influences from the sampling conditions and to compare classification efficiency starting either from audio traces or from kinematic data.…”