Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal
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 proces...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2015-03-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/837.pdf |
| _version_ | 1797418386296143872 |
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| author | Fred Hasselman |
| author_facet | Fred Hasselman |
| author_sort | Fred Hasselman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T06:32:00Z |
| format | Article |
| id | doaj.art-6f923de6ba074beca8de1f5e67dbd077 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:32:00Z |
| publishDate | 2015-03-01 |
| publisher | PeerJ Inc. |
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| spelling | doaj.art-6f923de6ba074beca8de1f5e67dbd0772023-12-03T11:03:58ZengPeerJ Inc.PeerJ2167-83592015-03-013e83710.7717/peerj.837837Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signalFred Hasselman0School of Pedagogical and Educational Science, Radboud University Nijmegen, The NetherlandsSeveral 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.https://peerj.com/articles/837.pdfSpeech perceptionDevelopmental dyslexiaRecurrence quantification analysisTheory evaluationRise time perception deficitStrong inference |
| spellingShingle | Fred Hasselman Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal PeerJ Speech perception Developmental dyslexia Recurrence quantification analysis Theory evaluation Rise time perception deficit Strong inference |
| title | Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal |
| title_full | Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal |
| title_fullStr | Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal |
| title_full_unstemmed | Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal |
| title_short | Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal |
| title_sort | classifying acoustic signals into phoneme categories average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal |
| topic | Speech perception Developmental dyslexia Recurrence quantification analysis Theory evaluation Rise time perception deficit Strong inference |
| url | https://peerj.com/articles/837.pdf |
| work_keys_str_mv | AT fredhasselman classifyingacousticsignalsintophonemecategoriesaverageanddyslexicreadersmakeuseofcomplexdynamicalpatternsandmultifractalscalingpropertiesofthespeechsignal |