Research into linguistic rhythm has been dominated by the idea that languages can be classified according to rhythmic templates, amenable to assessment by acoustic measures of vowel and consonant durations. This study tested predictions of two proposals explaining the bases of rhythmic typologies: the Rhythm Class Hypothesis which assumes that the templates arise from an extensive vs a limited use of durational contrasts, and the Control and Compensation Hypothesis which proposes that the templates are rooted in more vs less flexible speech production strategies. Temporal properties of segments, syllables and rhythmic feet were examined in two accents of British English, a "stress-timed" variety from Leeds, and a "syllable-timed" variety spoken by Panjabi-English bilinguals from Bradford. Rhythm metrics were calculated. A perception study confirmed that the speakers of the two varieties differed in their perceived rhythm. The results revealed that both typologies were informative in that to a certain degree, they predicted temporal patterns of the two varieties. None of the metrics tested was capable of adequately reflecting the temporal complexity found in the durational data. These findings contribute to the critical evaluation of the explanatory adequacy of rhythm metrics. Acoustic bases and limitations of the traditional rhythmic typologies are discussed.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1121/1.4919322 | DOI Listing |
R Soc Open Sci
September 2024
Centre for Neuroscience in Education, University of Cambridge, Cambridge, UK.
French and German poetry are classically considered to utilize fundamentally different linguistic structures to create rhythmic regularity. Their metrical rhythm structures are considered poetically to be very different. However, the biophysical and neurophysiological constraints upon the speakers of these poems are highly similar.
View Article and Find Full Text PDFLogoped Phoniatr Vocol
December 2024
Speech Prosody Studies Group, Dep. of Linguistics, State Univ. of Campinas, Campinas, Brazil.
Purpose: The analysis of acoustic parameters contributes to the characterisation of human communication development throughout the lifetime. The present paper intends to analyse suprasegmental features of European Portuguese in longitudinal conversational speech samples of three male public figures in uncontrolled environments across different ages, approximately 30 years apart.
Participants And Methods: Twenty prosodic features concerning intonation, intensity, rhythm, and pause measures were extracted semi-automatically from 360 speech intervals (3-4 interviews from each speaker x 30 speech intervals x 3 speakers) lasting between 3 to 6 s.
Hippocampus
January 2025
Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota, USA.
Because imagination activates the same neural circuits used in understanding the present, one can access that imagination even in non-linguistic animals through decoding techniques applied to large neural ensembles. This personal retrospective traces the history of the initial discovery that hippocampal theta sequences sweep forward to goals during moments of deliberation and discusses the history that was necessary to put ourselves in the position to recognize this signal. It also discusses how that discovery fits into the larger picture of hippocampal function and the concept of cognition as computation.
View Article and Find Full Text PDFPLoS One
December 2024
Department of Psychology, Centre for Neuroscience in Education, University of Cambridge, Cambridge, United Kingdom.
Cortical signals have been shown to track acoustic and linguistic properties of continuous speech. This phenomenon has been measured in both children and adults, reflecting speech understanding by adults as well as cognitive functions such as attention and prediction. Furthermore, atypical low-frequency cortical tracking of speech is found in children with phonological difficulties (developmental dyslexia).
View Article and Find Full Text PDFSci Rep
December 2024
Koios, London, UK.
This study introduces a novel method for predicting the Big Five personality traits through the analysis of speech samples, advancing the field of computational personality assessment. We collected data from 2045 participants who completed a self-reported Big Five personality questionnaire and provided free-form speech samples by introducing themselves without constraints on content. Using pre-trained convolutional neural networks and transformer-based models, we extracted embeddings representing both acoustic features (e.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!