The Phonetic Grounding of Prosody: Analysis and Visualisation Tools

Gibbon, Dafydd

doi:10.1007/978-3-031-05328-3_3

Cited by 2 publications

(5 citation statements)

References 17 publications

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“…Crucially, RFT is concerned not only with the contribution of AM to speech rhythm, but also with the contribution of FM (cf. also Varnet et al 2017;Gibbon 2018Gibbon , 2019Gibbon & Li 2019;Ludusan & Wagner 2020), and not only with identification of a rhythm but with simultaneous rhythms and rhythm variation over time. The RFT frequency domain definition of rhythm as oscillation implies that rhythms, whether AM or FM, have two main properties, SPECTRAL FREQUENCY and SPECTRAL MAGNITUDE, and that isochrony follows a fortiori from the concept of oscillation.…”

Section: Basics Of Rftmentioning

confidence: 99%

“…5. QUANTITATIVE MODULATION-THEORETIC SIGNAL PROCESSING, with production and perception models which represent low frequency components of the speech signal, ranging from the rhythmograms of Todd & Brown (1994) and Ludusan, Origlia & Cutugno (2011) through the coupled oscillators of Cummins & Port (1998), Barbosa (2002), Malisz et al (2016) and the sonority patterns of Galves et al (2002) and Fuchs & Wunder (2015), to the low frequency envelope spectrum of Tilsen & Johnson (2008), the cubic spline approximation approach of Tilsen & Arvaniti (2013), and the long-term LF spectrum approach of Gibbon (2018Gibbon ( , 2019.…”

Section: Qualitative and Quantitative Top-down And Bottom-up Approachesmentioning

confidence: 99%

“…The role of AM demodulation in speech rhythm has been studied relatively frequently, as discussed above. However, only a few studies relate to FM in rhythm modelling, including Cumming (2010), Varnet et al (2017, Gibbon (2018Gibbon ( , 2019, Gibbon & Li (2019) and Ludusan & Wagner (2020). FM has indeed been widely studied, but in the contexts of perceived prominence (cf.…”

Section: Frequency Domain and Time Domain Approximation Approachesmentioning

confidence: 99%

“…It has been a matter of debate to what extent both AM and FM influence the production and perception of rhythm, and relatively little quantitative analysis has been forthcoming (but cf. Cumming 2010;Varnet et al 2017;Gibbon 2018Gibbon , 2019Gibbon & Li 2019;Ludusan & Wagner 2020). In order to extend this domain, the RFA methodology, having been applied to AM rhythm analysis, was generalised and applied to FM rhythm analysis.…”

Section: English Stress-pitch Accent Sequencesmentioning

confidence: 99%

“… Qualitative algebraic models of stress hierarchies , from Chomsky, Halle & Lukoff (1956) through Chomsky & Halle (1968) and the metrical theory of Liberman & Prince (1977) to Selkirk (1984) and optimality theories (Prince & Smolensky 2004), sometimes accompanied by quantitative studies of phonetic correlates. Symbol–signal interface analysis , which combines qualitative with quantitative methods and relates linguistic units to intervals in the speech signal by annotating them with timestamps, often in an attempt to find ‘rhythm classes’ of languages in terms of irregularity of timing (Lehiste 1970, Jassem 1952, Roach 1982, Jassem et al 1984, Scott Isard & de Boysson-Bardies 1985, Ramus, Nespor & Mehler 1999, Grabe & Low 2002, Asu & Nolan 2006, Li, Yin & Zu 2006, Wagner 2007, Dellwo 2010, Yu & Gibbon 2015, Dihingia & Sarmah 2020). Quantitative modulation-theoretic signal processing , with production and perception models which represent low frequency components of the speech signal, ranging from the rhythmograms of Todd & Brown (1994) and Ludusan, Origlia & Cutugno (2011) through the coupled oscillators of Cummins & Port (1998), Barbosa (2002), Malisz et al (2016) and the sonority patterns of Galves et al (2002) and Fuchs & Wunder (2015), to the low frequency envelope spectrum of Tilsen & Johnson (2008), the cubic spline approximation approach of Tilsen & Arvaniti (2013), and the long-term LF spectrum approach of Gibbon (2018, 2019). …”

Section: Speech Rhythmsmentioning

confidence: 99%

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The rhythms of rhythm

Gibbon¹

2021

Journal of the International Phonetic Association

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The low frequency (LF) spectral analysis or ‘rhythm spectrum’ approach to the quantitative analysis and comparison of speech rhythms is extended beyond syllable or word rhythms to ‘rhetorical rhythms’ in read-aloud narratives, in a selection of exploratory scenarios, with the aim of developing a unified theory of speech rhythms. Current methodologies in the field are first discussed, then the choice of data is motivated and the modulation-theoretic rhythm spectrum and rhythm spectrogram approach is applied to the amplitude modulation (AM) and frequency modulation (FM) of speech. New concepts of rhythm formant, rhythm spectrogram and rhythm formant trajectory are introduced in the Rhythm Formant Theory (RFT) framework with its associated methodology Rhythm Formant Analysis (RFA) in order to capture second order regularities in the temporal variation of rhythms. The interaction of AM and FM rhythm factors is explored, contrasting English with Mandarin Chinese. The LF rhythm spectrogram is introduced in order to recover temporal information about long-term rhythms, and to investigate the configurative function of rhythm. The trajectory of highest magnitude frequencies through the component spectra of the LF spectrogram is extracted and applied in classifying readings in different languages and individual speaking styles using distance-based hierarchical clustering, and the existence of long-term second order ‘rhythms of rhythm’ in long narratives is shown. In the conclusion, pointers are given to the extension of this exploratory RFT rhythm approach for future quantitative confirmatory investigations.

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Section: Basics Of Rftmentioning

confidence: 99%

Section: Qualitative and Quantitative Top-down And Bottom-up Approachesmentioning

confidence: 99%

Section: Frequency Domain and Time Domain Approximation Approachesmentioning

confidence: 99%

Section: English Stress-pitch Accent Sequencesmentioning

confidence: 99%

Section: Speech Rhythmsmentioning

confidence: 99%

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The rhythms of rhythm

Gibbon¹

2021

Journal of the International Phonetic Association

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Cross-linguistic rhythm analysis of Mising and Assamese

Gogoi,

Sarmah,

Prasanna

2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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The objective of the current study is to explore a quantitative frequency domain technique to evaluate rhythm in spontaneous speech data of 19 native speakers of Mising and Assamese, two low-resourced languages spoken in Assam, North-East India. The concept of analyzing speech rhythm using amplitude modulation (AM) low-frequency (LF) spectrum, also known as rhythm formant analysis (RFA), is initially put forth by Gibbon and Li [ 17 ]. We propose three features from rhythm formants of the LF spectrum and also explore discrete cosine transform (DCT)–based characterization of the retrieved LF spectrum. We aim to distinguish the rhythm of Assamese and two Mising dialects, namely Pagro and Delu, with the aid of machine learning techniques fed with the derived features as input. We have observed that the features are efficient in classifying Assamese vs Pagro and Assamese vs Delu with an accuracy of 92.73% and 91.15%, respectively. The experimental analysis further reveals that Assamese is rhythmically closer to Delu than Pagro.

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The Phonetic Grounding of Prosody: Analysis and Visualisation Tools

Cited by 2 publications

References 17 publications

The rhythms of rhythm

The rhythms of rhythm

Cross-linguistic rhythm analysis of Mising and Assamese

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