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Phrase-level prosodic prominence in American English is understood, in the AM tradition, to be marked by pitch accents. While such prominences are characterized via tonal labels in ToBI (e.g. H*), their cues are not exclusively in the pitch domain: timing, loudness and voice quality are known to contribute to prominence perception. All of these cues occur with a wide degree of variability in naturally produced speech, and this variation may be informative. In this study, we advance towards a system of explicit labelling of individual cues to prosodic structure, here focusing on phrase-level prominence. We examine correlations between the presence of a set of 6 cues to prominence (relating to segment duration, loudness, and non-modal phonation, in addition to f0) and pitch accent labels in a corpus of ToBI-labelled American English speech. Results suggest that tokens with more cues are more likely to receive a pitch accent label.
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Machine Learning Facilitated Investigations of Intonational Meaning: Prosodic Cues to Epistemic Shifts in American English Utterances
Machine Learning Facilitated Investigations of Intonational Meaning: Prosodic Cues to Epistemic Shifts in American English Utterances Authors: Veilleux, Shattuck-Hufnagel, Jeong, Brugos, Ahn This work analyzes experimentally elicited speech to capture the relationship between prosody and semantic/pragmatic meanings. Production prompts were comicstrips where contexts were manipulated along axes prominently discussed in sem/prag literature. Participants were tasked with reading lines as the speaker would, uttering a target phrase communicating a proposition p (e.g., “only marble is available”) to a hearer who had epistemic authority on p. Prompts varied whether the speaker’s initial belief (prior bias) was confirmed (condition A: bias=p) or corrected (condition B: bias=¬p); this meaning difference was reinforced by response particles (A: “okay so” vs. B: “oh really”) preceding the target phrase. Over 475 productions were annotated with phonologically-informed phonetic labels (PoLaR). To model many-to-many mappings between features (prosodic form) and classification (sem/prag meaning), Random Forests were designed on labels and derived measures (including f0 ranges, slopes, TCoG) from 299 recordings — classifying meaning with high accuracy (>85%). RFs identified condition-distinguishing prosodic cues in both response particle and target phrases, leading to questions of how/whether functionally-overlapping lexical content might affect prosodic realization. Moreover, RFs identified phrase-final f0 as important, leading to deeper edge-tone explorations. These highlight how explanatory ML models can help iteratively improve targeted analysis.
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- PAR ID:
- 10527900
- Publisher / Repository:
- ISCA
- Date Published:
- Page Range / eLocation ID:
- 931 to 935
- Subject(s) / Keyword(s):
- speech recognition human-computer interaction computational paralinguistics intonational meaning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.21437/SpeechProsody.2024-188
