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Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal.

Hasselman F - PeerJ (2015)

Bottom Line: Studies examining these temporal processing deficit hypotheses do not employ measures that quantify the temporal dynamics of stimuli.It seems unlikely that participants used any of the features that are traditionally associated with accounts of (impaired) speech perception.It is suggested that the results imply that the differences in speech perception performance between average and dyslexic readers represent a scaled continuum rather than being caused by a specific deficient component.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Pedagogical and Educational Science, Radboud University Nijmegen , The Netherlands.

ABSTRACT
Several competing aetiologies of developmental dyslexia suggest that the problems with acquiring literacy skills are causally entailed by low-level auditory and/or speech perception processes. The purpose of this study is to evaluate the diverging claims about the specific deficient peceptual processes under conditions of strong inference. Theoretically relevant acoustic features were extracted from a set of artificial speech stimuli that lie on a /bAk/-/dAk/ continuum. The features were tested on their ability to enable a simple classifier (Quadratic Discriminant Analysis) to reproduce the observed classification performance of average and dyslexic readers in a speech perception experiment. The 'classical' features examined were based on component process accounts of developmental dyslexia such as the supposed deficit in Envelope Rise Time detection and the deficit in the detection of rapid changes in the distribution of energy in the frequency spectrum (formant transitions). Studies examining these temporal processing deficit hypotheses do not employ measures that quantify the temporal dynamics of stimuli. It is shown that measures based on quantification of the dynamics of complex, interaction-dominant systems (Recurrence Quantification Analysis and the multifractal spectrum) enable QDA to classify the stimuli almost identically as observed in dyslexic and average reading participants. It seems unlikely that participants used any of the features that are traditionally associated with accounts of (impaired) speech perception. The nature of the variables quantifying the temporal dynamics of the speech stimuli imply that the classification of speech stimuli cannot be regarded as a linear aggregate of component processes that each parse the acoustic signal independent of one another, as is assumed by the 'classical' aetiologies of developmental dyslexia. It is suggested that the results imply that the differences in speech perception performance between average and dyslexic readers represent a scaled continuum rather than being caused by a specific deficient component.

No MeSH data available.


Related in: MedlinePlus

MF-DFA.Multifractal Detrended Fluctuation Analysis of the 40 stimuli.
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fig-9: MF-DFA.Multifractal Detrended Fluctuation Analysis of the 40 stimuli.

Mentions: To obtain a spectrum of scaling exponents for each q-order, the 4 steps of standard DFA are repeated for a q-continuum, which typically ranges from −10 to 10. The left column of Fig. 9 shows for the 4 × 10 stimuli their fluctuation functions of order q = [ − 5, − 2, 0, 2, 5]. The black dotted power law at q = 2 represent the fluctuation function of stimulus 1 that is show in the bottom row of Fig. 9. For each of the 40 stimuli, a 101 step q-continuum was estimated ranging from q = − 10 to q = 10 (including q = 0). The scaling exponents H(q) are the slopes of those 101 fluctuation functions (Table S1 lists for each stimulus the average and SD of the norm of the residual after regression). Those slopes are plotted against q in the middle column of Fig. 9. If the stimuli were monofractals, there would have been no dependence of the scaling exponent H(q) on the q-order for which it was calculated. The plots in the middle column of Fig. 9 would all have been horizontal lines (see e.g., Fig. 1D in Kantelhardt et al., 2002, p. 94). Here, it is clearly the case that all the stimuli used in the study should be considered multifractal signals. The multifractal spectrum (right column of Fig. 9) is a representation of the generalized scaling exponents (now called singularity, Hölder, or generalized Hurst exponents) against D(q), the q-order singularity dimension (the calculation of D(q) is not shown here, see Ihlen, 2012, for details).


Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal.

Hasselman F - PeerJ (2015)

MF-DFA.Multifractal Detrended Fluctuation Analysis of the 40 stimuli.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4380160&req=5

fig-9: MF-DFA.Multifractal Detrended Fluctuation Analysis of the 40 stimuli.
Mentions: To obtain a spectrum of scaling exponents for each q-order, the 4 steps of standard DFA are repeated for a q-continuum, which typically ranges from −10 to 10. The left column of Fig. 9 shows for the 4 × 10 stimuli their fluctuation functions of order q = [ − 5, − 2, 0, 2, 5]. The black dotted power law at q = 2 represent the fluctuation function of stimulus 1 that is show in the bottom row of Fig. 9. For each of the 40 stimuli, a 101 step q-continuum was estimated ranging from q = − 10 to q = 10 (including q = 0). The scaling exponents H(q) are the slopes of those 101 fluctuation functions (Table S1 lists for each stimulus the average and SD of the norm of the residual after regression). Those slopes are plotted against q in the middle column of Fig. 9. If the stimuli were monofractals, there would have been no dependence of the scaling exponent H(q) on the q-order for which it was calculated. The plots in the middle column of Fig. 9 would all have been horizontal lines (see e.g., Fig. 1D in Kantelhardt et al., 2002, p. 94). Here, it is clearly the case that all the stimuli used in the study should be considered multifractal signals. The multifractal spectrum (right column of Fig. 9) is a representation of the generalized scaling exponents (now called singularity, Hölder, or generalized Hurst exponents) against D(q), the q-order singularity dimension (the calculation of D(q) is not shown here, see Ihlen, 2012, for details).

Bottom Line: Studies examining these temporal processing deficit hypotheses do not employ measures that quantify the temporal dynamics of stimuli.It seems unlikely that participants used any of the features that are traditionally associated with accounts of (impaired) speech perception.It is suggested that the results imply that the differences in speech perception performance between average and dyslexic readers represent a scaled continuum rather than being caused by a specific deficient component.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Pedagogical and Educational Science, Radboud University Nijmegen , The Netherlands.

ABSTRACT
Several competing aetiologies of developmental dyslexia suggest that the problems with acquiring literacy skills are causally entailed by low-level auditory and/or speech perception processes. The purpose of this study is to evaluate the diverging claims about the specific deficient peceptual processes under conditions of strong inference. Theoretically relevant acoustic features were extracted from a set of artificial speech stimuli that lie on a /bAk/-/dAk/ continuum. The features were tested on their ability to enable a simple classifier (Quadratic Discriminant Analysis) to reproduce the observed classification performance of average and dyslexic readers in a speech perception experiment. The 'classical' features examined were based on component process accounts of developmental dyslexia such as the supposed deficit in Envelope Rise Time detection and the deficit in the detection of rapid changes in the distribution of energy in the frequency spectrum (formant transitions). Studies examining these temporal processing deficit hypotheses do not employ measures that quantify the temporal dynamics of stimuli. It is shown that measures based on quantification of the dynamics of complex, interaction-dominant systems (Recurrence Quantification Analysis and the multifractal spectrum) enable QDA to classify the stimuli almost identically as observed in dyslexic and average reading participants. It seems unlikely that participants used any of the features that are traditionally associated with accounts of (impaired) speech perception. The nature of the variables quantifying the temporal dynamics of the speech stimuli imply that the classification of speech stimuli cannot be regarded as a linear aggregate of component processes that each parse the acoustic signal independent of one another, as is assumed by the 'classical' aetiologies of developmental dyslexia. It is suggested that the results imply that the differences in speech perception performance between average and dyslexic readers represent a scaled continuum rather than being caused by a specific deficient component.

No MeSH data available.


Related in: MedlinePlus