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Abstract Whereas some authors claim that the distribution of prenuclear accents in English largely follows from rhythmic and other non-informational considerations, other authors report a small but meaningful effect of prenuclear accents on the interpretation of sentences. In this paper we report on an experiment where native English speakers were asked to repeat stimulus sentences with one of three different accentual patterns on a word in sentence-initial prenuclear position: unaccented, with a high pitch accent on the syllable with primary stress or with a high accent on an earlier syllable with secondary stress. Participants were moderately successful in reproducing the intonational patterns. The early high accent pattern was reproduced particularly well. An automatic classification algorithm nevertheless produced four clusters of contours, instead of the three patterns present in the stimuli. Two distinct contours were used to signal the presence of a high tone before the syllable with primary stress. We conclude that the early high accent pattern is a strong attractor in imitations, but it was implemented with F0 trajectories that would be analyzed as phonologically different, suggesting an equivalence class of prenuclear contours. We also note a preference for rhythmic anchoring in the prenuclear position. 

Cluster analysis on time-series f0 data is an increasingly popular method in intonation research. There are a number of methodological decisions to take when applying cluster analysis. Crucially, these decisions may affect the clustering results, potentially also the conclusions of the research. This paper investigates the extent to which the choice for either K-means or hierarchical clustering, two of the most popular clustering methods, leads to grouping differences that are potentially relevant for intonation research. This is tested using a dataset of f0 measures taken from imitated intonation patterns in American English. The analysis concerns a generic correlation test between K-means and hierarchical clustering outcomes as well as a number of specific measures assessing partitioning quality and f0 contour differences. The results show that both cluster methods generally show very similar outcomes, although considerable differences for specific clusterings might occur. 

The Autosegmental-Metrical model of American English distinguishes three pitch accents with rising F0 trajectories (H*, L+H*, L*+H), differing in peak alignment and presence vs. absence of a low pitch marking the rise onset. Empirical studies report additional distinctions in the dynamics and scaling of the F0 rise, raising the question of which properties best capture variation among accents. We use functional principal components analysis (FPCA) to examine dynamic properties of accentual F0 trajectories in data from an intonation imitation experiment. F0 trajectories from 70 speakers producing rising accents on the phrase-final (nuclear) accented word were submitted to FPCA. The first three PCs account for 95% of variation in F0 trajectories and each shows significant differences between the three rising accents. Variation in PC1 primarily relates to differences in the overall F0 level of the trajectory, PC2 captures differences in rise shape (scooped vs. domed rise) and PC3 captures fine variation from a following Low phrase accent. Alignment distinctions are distributed across all three PCs. Examination of individual speakers shows all use PC1 and PC2 to some degree to distinguish rising accents, with no trading relations. Rises are variously implemented through level or shape distinctions, to varying degrees across individuals 

We present two experiments aimed at testing the nature of intonational categories through the lens of enhancement. In an imitative speech production paradigm, speakers heard a model intonational tune and were prompted to reproduce that tune on a new sentence in which the syllable count of the word carrying the tune varied. Using the prevalent auto-segmental metrical model of American English as a basis for potential tune categories, we test how distinctions among tunes are enhanced across different metrical structures. First, with a clustering analysis, we find that not all predicted distinctions are emergent. Secondly, only the largest distinctions, those that emerge in the clustering analysis, are enhanced as a function of metrical structure. Measurable differences between tunes which cluster together are detectable, but critically, are not enhanced. We discuss what these results mean for the nature and number of intonational categories in the system. 

Recent work on rising declaratives proposes a distinction between steep inquisitive rising declaratives and shallow assertive rising declaratives. Yet, it is unclear whether this contrast arises from a phonological distinction of the pitch accent used or a phonetic distinction in the scaling of the boundary tone target. In two perception experiments, we evaluate the contributions of pitch accent and boundary tone in the interpretation of assertive force. In Exp. 1, we find a counterintuitive result for the weighting of pitch accent, which is better understood from the perspective of the Tonal Center of Gravity. This perspective provides a path forward for Exp. 2, which shows no evidence of a contribution from the pitch accent in the interpretation of assertive force. Results speak against a phonological contrast in subtypes of rising declaratives and suggest a need for more narrow investigation in the phonetic domain. 

We test a subset of intonational contrasts proposed in the Autosegmental-Metrical model for American English for evidence of contrast enhancement in phonologically and phonetically longer vs. shorter intervals. F0 trajectories were assessed from 32 speakers’ imitated productions of six tonally distinct tunes, e.g., HHH, HHL. Maximally three tune shapes emerge from clustering analyses of imitated f0 trajectories, each cluster comprising imitations of two phonetically similar but phonologically distinct tunes. We find enhancement of tune contrasts between the emergent clusters in measures of f0 differences (RMSD, end f0, center of gravity). There is no evidence of enhancement for phonetically similar tunes grouped within the same cluster, though fine-grained phonetic distinctions are detected for these “lost” tune contrasts, suggesting a reanalysis as within-category variation. 

What is the minimal mathematical model that can generate the F0 trajectories for a system of pitch accents? In this work, we propose a nonlinear coupled dynamical systems theory of American English pitch accents with a single basic parameter. As that parameter increases, F0 profiles for different pitch accents are generated. The terms in the differential equation are based on a novel dynamical analysis of a large database of F0 productions in terms of measurements of F0 peak, peak velocity, and the time to achieve peak velocity. We describe the basic dynamical properties of pitch accents in our database and argue for the proposed model as the simplest one that realizes all the major dynamic F0 properties of the pitch accent system. We argue that the proposed model describes both abstract phonological and concrete phonetic aspects of the system. 

In Autosegmental-Metrical models of intonational phonology, different types of pitch accents, phrase accents, and boundary tones concatenate to create a set of phonologically distinct phrase-final nuclear tunes. This study asks if an eight-way distinction in nuclear tune shape in American English, predicted from the combination of two (monotonal) pitch accents, two phrase accents, and two boundary tones, is evident in speech production and in speech perception. F0 trajectories from a large-scale imitative speech production experiment were analyzed using bottom-up(k-means) clustering, neural net classification, GAMM modeling, and modeling of turning point alignment. Listeners’ perception of the same tunes is tested in a perceptual discrimination task and related to the imitation results. Emergent grouping of tunes in the clustering analysis, and related classification accuracy from the neural net, show a merging of some of the predicted distinctions among tunes whereby tune shapes that vary primarily in the scaling of final f0 are not reliably distinguished. Within five emergent clusters, subtler distinctions among tunes are evident in GAMMs and f0 turning point modeling. Clustering of individual participants’ production data shows a range of partitions of the data, with nearly all participants making a primary distinction between a class of High-Rising and Non-High-Rising tunes, and with up to four secondary distinctions among the non-Rising class. Perception results show a similar pattern, with poor pairwise discrimination for tunes that differ primarily, but by a small degree, in final f0, and highly accurate discrimination when just one member of a pair is in the High-Rising tune class. Together, the results suggest a hierarchy of distinctiveness among nuclear tunes, with a robust distinction based on holistic tune shape and poorly differentiated distinctions between tunes with the same holistic shape but small differences in final f0. The observed distinctions from clustering, classification, and perception analyses align with the tonal specification of a binary pitch accent contrast {H*, L*} and a maximally ternary {H%, M%, L%} boundary tone contrast; the findings do not support distinct tonal specifications for the phrase accent and boundary tone from the AM model.  

An algorithm for detecting sudden jumps in measured F0, which are likely to be inaccurate measures, is introduced. The method computes sample-to-sample differences in F0 and, based on a user-defined threshold, determines whether a difference is larger than naturally produced F0 velocities, thus, flagging it as an error. Various parameter settings are evaluated on a corpus of 30 American English speakers producing different intonational patterns, for which F0 tracking errors were manually checked. The paper concludes in recommending settings for the algorithm and ways in which it can be used to facilitate analyses of F0 in speech research.