The role of syllable intensity in between-speaker rhythmic variability

Dellwo, Volker

doi:10.1558/ijsll.v23i2.30345

Cited by 19 publications

(23 citation statements)

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“…10,11 Such kinematic characteristics are assumed to be the reason for speakerspecific production of prosodic duration 5,6 and intensity variabilities. 7,12 The present research underlies the assumption that the intensity contour shape might be closely related to the articulatory movements responsible for the changes of mouth opening area in an utterance. Such a view is supported by Summerfield 13 who held that the amplitude envelope co-varied with the area of mouth opening, and Chandrasekaran et al 2 who reported strong empirical evidence for Summerfield's claim.…”

Section: Introductionmentioning

confidence: 72%

“…17 It was demonstrated to be particularly suitable for summarizing the sequential variability in speech over the course of an entire utterance. [5][6][7]17 We notated these measures as MEAN_ The intensity contour (lower plot) was calculated from the speech waveform (upper plot). The amplitude envelope (superimposed over the waveform in the upper plot) was used to facilitate locating the peak and trough points (t P and t T ).…”

Section: Measurement Of Intensity Dynamicsmentioning

confidence: 99%

“…[5][6][7] The rationale of these studies is that articulatory movements are comparable to other domains of human movements (e.g., gait and typing) where individual differences are conspicuous. 3,[5][6][7] Such individualities are related to both individual neurological dispositions, which constrain the motor control over the respective body parts, 8 and ontogenetic anatomical characteristics of moving body parts, which shape their biomechanical properties. 9 As a specialized domain of human motor behavior, articulation also reflects speaker individualities because of anatomical idiosyncrasies of the articulators and the way speakers acquired control over them.…”

Section: Introductionmentioning

confidence: 99%

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Between-speaker variability in temporal organizations of intensity contours

Dellwo

2017

The Journal of the Acoustical Society of America

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Intensity contours of speech signals were sub-divided into positive and negative dynamics. Positive dynamics were defined as the speed of increases in intensity from amplitude troughs to subsequent peaks, and negative dynamics as the speed of decreases in intensity from peaks to troughs. Mean, standard deviation, and sequential variability were measured for both dynamics in each sentence. Analyses showed that measures of both dynamics were separately classified and between-speaker variability was largely explained by measures of negative dynamics. This suggests that parts of the signal where intensity decreases from syllable peaks are more speaker-specific. Idiosyncratic articulation may explain such results. Abstract: Intensity contours of speech signals were sub-divided into positive and negative dynamics. Positive dynamics were defined as the speed of increases in intensity from amplitude troughs to subsequent peaks, and negative dynamics as the speed of decreases in intensity from peaks to troughs. Mean, standard deviation, and sequential variability were measured for both dynamics in each sentence. Analyses showed that measures of both dynamics were separately classified and betweenspeaker variability was largely explained by measures of negative dynamics. This suggests that parts of the signal where intensity decreases from syllable peaks are more speaker-specific. Idiosyncratic articulation may explain such results.

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Section: Introductionmentioning

confidence: 72%

Section: Measurement Of Intensity Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Between-speaker variability in temporal organizations of intensity contours

Dellwo

2017

The Journal of the Acoustical Society of America

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“…We also provide a Praat script for this purpose, which researchers interested in cochlear implant design, speech chimera, or amplitude envelope-based approaches to speech rhythm [20,21,22,23,24,25] may find useful. Depending on the need, this script can be easily modified into a procedure to be implemented in a bigger program, or saved as a button in the Praat object window.…”

Section: Resultsmentioning

confidence: 99%

A Praat-Based Algorithm to Extract the Amplitude Envelope and Temporal Fine Structure Using the Hilbert Transform

Dellwo

2016

Interspeech 2016

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A speech signal can be viewed as a high frequency carrier signal containing the temporal fine structure (TFS) that is modulated by a low frequency envelope (ENV). A widely used method to decompose a speech signal into the TFS and ENV is the Hilbert transform. Although this method has been available for about one century and is widely applied in various kinds of speech processing tasks (e.g. speech chimeras), there are only very few speech processing packages that contain readily available functions for the Hilbert transform, and there is very little textbook type literature tailored for speech scientists to explain the processes behind the transform. With this paper we provide the code for carrying out the Hilbert operation to obtain the TFS and ENV in the widely used speech processing software Praat, and explain the basics of the procedure. To verify our code, we compare the Hilbert transform in Praat with a widely applied function for the same purpose in MATLAB ("hilbert(...)"). We can confirm that both methods arrive at identical outputs. AbstractA speech signal can be viewed as a high frequency carrier signal containing the temporal fine structure (TFS) that is modulated by a low frequency envelope (ENV). A widely used method to decompose a speech signal into the TFS and ENV is the Hilbert transform. Although this method has been available for about one century and is widely applied in various kinds of speech processing tasks (e.g. speech chimeras), there are only very few speech processing packages that contain readily available functions for the Hilbert transform, and there is very little textbook type literature tailored for speech scientists to explain the processes behind the transform. With this paper we provide the code for carrying out the Hilbert operation to obtain the TFS and ENV in the widely used speech processing software Praat, and explain the basics of the procedure. To verify our code, we compare the Hilbert transform in Praat with a widely applied function for the same purpose in MATLAB ("hilbert(...)"). We can confirm that both methods arrive at identical outputs.

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“…Speech rhythm is multidimensional, 1 yet the majority of rhythm models were heavily based on the durational dimension in some way. 1,2 The present study investigated the development of speech rhythm in first language (L1) from the perspective of intensity variability between syllables. Such intensity-based rhythm measures (intensity measures hereafter) may augment our understanding of the developmental patterns of L1 rhythm beyond the durational dimension.…”

Section: Introductionmentioning

confidence: 99%

Development of speech rhythm in first language: The role of syllable intensity variability

2018

The Journal of the Acoustical Society of America

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The opening-closing alternations of the mouth were viewed as the articulatory basis of speech rhythm. Such articulatory cycles have been observed to highly correlate with the intensity curve of the speech signal. Analysis of the intensity variability in English monolingual children and adults revealed that (1) adults showed significantly smaller intensity variability than children, and (2) intensity variability decreased from intermediate-aged children to older children. Maturation of articulatory motor control is likely to be the main reason for the reduced variability in articulatory cycles, and hence smaller intensity variability in adults and older children.

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The role of syllable intensity in between-speaker rhythmic variability

Cited by 19 publications

References 0 publications

Between-speaker variability in temporal organizations of intensity contours

Between-speaker variability in temporal organizations of intensity contours

A Praat-Based Algorithm to Extract the Amplitude Envelope and Temporal Fine Structure Using the Hilbert Transform

Development of speech rhythm in first language: The role of syllable intensity variability

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