Dynamic properties of an acoustic tube: Prediction of vowel systems

Carré, René

doi:10.1016/j.specom.2008.05.015

Cited by 13 publications

(8 citation statements)

References 56 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e other formant frequencies of the Russian vowels are "deep" in the simulated spaces. Figure 6 shows the results that coincide with (Carré, 2009) although the resonant frequencies area was analyzed and simulated with a di erent method. …”

Section: Mvs Estimation Of An Adult Malesupporting

confidence: 53%

10.5937/specedreh13-6038 = Maximal vowel space method in analysis of vowels in prelingual speech phase

Vojnović¹,

Bogavac²,

Jeličić-Dobrijević³

2014

Spec eduk i rehabilitacija

View full text Add to dashboard Cite

e main problems in the analysis of vowels which occur in prelingual speech phase are centralization of utterance and unknown dimension of vocal tract. Most researches in this eld are based on the analysis of maximal vowel space (MVS) because discrimination of vowels is very di cult in this early period. MVS analysis includes the estimation of vocal tract (VT) physical dimensions. e aim of this research was to estimate and de ne changes in vowel pronunciation during prelingual speech phase. e analysis and voice recording were performed in a two month old child until he turned one. e recording was performed in 42 sessions, on average 4 sessions every month. Sound segments that look like vowel pronunciation were extracted from the recordings and were used for the formant frequencies estimation by PRAAT so ware. e Burg method was used for formant frequency estimation. Research results showed that MVS can be used in diagnostic procedure from a child's earliest age. MVS analysis is appropriate for a child's earliest age as a child needs to pronounce individual phonemes, and does not need to respond to speech stimuli. ese results need to be con rmed on a larger sample when extended analysis should de ne criteria for discrimination of typical and atypical formant frequencies.

show abstract

Section: Mvs Estimation Of An Adult Malesupporting

confidence: 53%

10.5937/specedreh13-6038 = Maximal vowel space method in analysis of vowels in prelingual speech phase

Vojnović¹,

Bogavac²,

Jeličić-Dobrijević³

2014

Spec eduk i rehabilitacija

View full text Add to dashboard Cite

show abstract

“…Researchers have demonstrated that many acoustic dimensions that contribute to speech perception are perceptually integral (Kingston, Diehl, Kirk, & Castleman, 2008;Macmillan, Kingston, Thorburn, Walsh Dickey, & Bartels, 1999). This perceptual integrality of speech dimensions may be driven by physical constraints on articulatory mechanisms that renders acoustic input dimensions to be interdependent (Carré, 2009) in the manner of the Bnatural co-variation^that we posit to explain the relationship between CF and MF in the current study. Alternatively, this integrality may also be a consequence of some psychoacoustic similarity between the dimensions (Diehl, 2008;Kingston et al, 2008;Macmillan et al, 1999).…”

Section: Implications For Models Of Category Learningmentioning

confidence: 79%

Perceptual dimensions influence auditory category learning

Roark

Holt

2019

Atten Percept Psychophys

View full text Add to dashboard Cite

Human category learning appears to be supported by dual learning systems. Previous research indicates the engagement of distinct neural systems in learning categories that require selective attention to dimensions versus those that require integration across dimensions. This evidence has largely come from studies of learning across perceptually separable visual dimensions, but recent research has applied dual system models to understanding auditory and speech categorization. Since differential engagement of the dual learning systems is closely related to selective attention to input dimensions, it may be important that acoustic dimensions are quite often perceptually integral and difficult to attend to selectively. We investigated this issue across artificial auditory categories defined by center frequency and modulation frequency acoustic dimensions. Learners demonstrated a bias to integrate across the dimensions, rather than to selectively attend, and the bias specifically reflected a positive correlation between the dimensions. Further, we found that the acoustic dimensions did not equivalently contribute to categorization decisions. These results demonstrate the need to reconsider the assumption that the orthogonal input dimensions used in designing an experiment are indeed orthogonal in perceptual space as there are important implications for category learning.

show abstract

“…Speech categories are highly multidimensional, with dimensions that are difficult to describe verbally and are often perceptually integral (Francis et al, 2000;Grau & Kemler Nelson, 1988;Hillenbrand et al, 1995). Further, the physical constraints on articulatory mechanisms mean that the various acoustic input dimensions across which speech sounds can vary are not necessarily independent (Carré, 2009;Diehl, 2008;Kingston, Diehl, Kirk, & Castleman, 2008;Macmillan, Kingston, Thorburn, Walsh Dickey, & Bartels, 1999). As a result, there are likely to be interdependencies that would result in the kind of 'natural covariation' that we posit might explain the relationship between CF and MF in the current study.…”

Section: Perceptual Dimensions Impact Category Learning 32mentioning

confidence: 99%

Perceptual dimensions influence auditory category learning

Roark¹,

Holt²

2018

Preprint

View full text Add to dashboard Cite

Human category learning appears to be supported by dual learning systems. Previous research indicates the engagement of distinct neural systems in learning categories that require selective attention to dimensions versus those that require integration across dimensions. This evidence has largely come from studies of learning across perceptually separable visual dimensions, but recent research has applied dual systems models to understanding auditory and speech categorization. Since differential engagement of the dual learning systems is closely related to selective attention to input dimensions, it may be important that acoustic dimensions are quite often perceptually integral and difficult to attend to selectively. We investigated this issue across artificial auditory categories defined by center frequency and modulation frequency acoustic dimensions. Learners demonstrated a bias to integrate across the dimensions, rather than to selectively attend and the bias specifically reflected a positive correlation between the dimensions. Further, we found that the acoustic dimensions did not equivalently contribute to categorization decisions. These results demonstrate the need to reconsider the assumption that the orthogonal input dimensions used in designing an experiment are indeed orthogonal in perceptual space as there are important implications for category learning.

show abstract

Dynamic properties of an acoustic tube: Prediction of vowel systems

Abstract: International audienc

Cited by 13 publications

References 56 publications

10.5937/specedreh13-6038 = Maximal vowel space method in analysis of vowels in prelingual speech phase

10.5937/specedreh13-6038 = Maximal vowel space method in analysis of vowels in prelingual speech phase

Perceptual dimensions influence auditory category learning

Perceptual dimensions influence auditory category learning

Contact Info

Product

Resources

About