Dynamic Formant Trajectories in German Read Speech: Impact of Predictability and Prominence

Abstract

Phonetic structures expand temporally and spectrally when they are difficult to predict from their context. To some extent, effects of predictability are modulated by prosodic structure. So far, studies on the impact of contextual predictability and prosody on phonetic structures have neglected the dynamic nature of the speech signal. This study investigates the impact of predictability and prominence on the dynamic structure of the first and second formants of German vowels. We expect to find differences in the formant movements between vowels standing in different predictability contexts and a modulation of this effect by prominence. First and second formant values are extracted from a large German corpus. Formant trajectories of peripheral vowels are modeled using generalized additive mixed models, which estimate nonlinear regressions between a dependent variable and predictors. Contextual predictability is measured as biphone and triphone surprisal based on a statistical German language model. We test for the effects of the information-theoretic measures surprisal and word frequency, as well as prominence, on formant movement, while controlling for vowel phonemes and duration. Primary lexical stress and vowel phonemes are significant predictors of first and second formant trajectory shape. We replicate previous findings that vowels are more dispersed in stressed syllables than in unstressed syllables. The interaction of stress and surprisal explains formant movement: unstressed vowels show more variability in their formant trajectory shape at different surprisal levels than stressed vowels. This work shows that effects of contextual predictability on fine phonetic detail can be observed not only in pointwise measures but also in dynamic features of phonetic segments.

Being a round /y/: An acoustic description of high front vowels in Singapore Mandarin elicited by speakers with different bilingual balance in Mandarin and English

“…Since each participant was asked to use the word in the picture at least once in their description, this allows adequate dynamic speech data to be fitted in GAMMs. Previous research on vowel trajectories suggests that GAMMs provide a framework to model flexible non-linear relationships and can capture the dynamic pattern of formants of vowels, for example in German (Brandt et al, 2021;Carignan et al, 2020).…”

Understanding the Phonetic Characteristics of Speech Under Uncertainty—Implications of the Representation of Linguistic Knowledge in Learning and Processing

“…Lower uncertainty has been shown to be associated with shorter words, syllables and segments (Aylett and Turk, 2004 ; Cohen Priva, 2015 ) and more centralized vowels (Wright, 2004 ; Aylett and Turk, 2006 ; Munson, 2007 ; Malisz et al, 2018 ; Brandt et al, 2019 ). This has been demonstrated by studies that operationalized uncertainty by means of word frequency (Wright, 1979 , 2004 ; Fosler-Lussier and Morgan, 1999 ; Bybee, 2002 ), conditional probability (Jurafsky et al, 2001a , b ; Aylett and Turk, 2004 ; Bell et al, 2009 ), or informativity (Cohen Priva, 2015 ; Schulz et al, 2016 ; Malisz et al, 2018 ; Brandt et al, 2019 , 2021 ). Aylett and Turk ( 2004 , 2006 )'s Smooth Signal Redundancy Hypothesis explains these reduction phenomena from an information theoretic perspective (Shannon, 1948 ), arguing that the amount of information in the speech signal is balanced against the amount of information conveyed at the syntagmatic level.…”