Dataset Information

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

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.

SUBMITTER: Hasselman F

PROVIDER: S-EPMC4380160 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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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 Fred F

PeerJ 20150326

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 ...[more]

PMID: 25834769

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Project description:Neuroimaging work from developmental and reading intervention research has suggested a cause of reading failure may be lack of engagement of parietotemporal cortex during initial acquisition of grapheme-phoneme (letter-sound) mappings. Parietotemporal activation increases following grapheme-phoneme learning and successful reading intervention. Further, stimulation of parietotemporal cortex improves reading skill in lower ability adults. However, it is unclear whether these improvements following stimulation are due to enhanced grapheme-phoneme mapping abilities. To test this hypothesis, we used transcranial direct current stimulation (tDCS) to manipulate parietotemporal function in adult readers as they learned a novel artificial orthography with new grapheme-phoneme mappings. Participants received real or sham stimulation to the left inferior parietal lobe (L IPL) for 20 min before training. They received explicit training over the course of 3 days on 10 novel words each day. Learning of the artificial orthography was assessed at a pre-training baseline session, the end of each of the three training sessions, an immediate post-training session and a delayed post-training session about 4 weeks after training. Stimulation interacted with baseline reading skill to affect learning of trained words and transfer to untrained words. Lower skill readers showed better acquisition, whereas higher skill readers showed worse acquisition, when training was paired with real stimulation, as compared to readers who received sham stimulation. However, readers of all skill levels showed better maintenance of trained material following parietotemporal stimulation, indicating a differential effect of stimulation on initial learning and consolidation. Overall, these results indicate that parietotemporal stimulation can enhance learning of new grapheme-phoneme relationships in readers with lower reading skill. Yet, while parietotemporal function is critical to new learning, its role in continued reading improvement likely changes as readers progress in skill.

Project description:This study was aimed at predicting individual differences in text reading fluency. The basic proposal included two factors, i.e., the ability to decode letter strings (measured by discrete pseudo-word reading) and integration of the various sub-components involved in reading (measured by Rapid Automatized Naming, RAN). Subsequently, a third factor was added to the model, i.e., naming of discrete digits. In order to use homogeneous measures, all contributing variables considered the entire processing of the item, including pronunciation time. The model, which was based on commonality analysis, was applied to data from a group of 43 typically developing readers (11- to 13-year-olds) and a group of 25 chronologically matched dyslexic children. In typically developing readers, both orthographic decoding and integration of reading sub-components contributed significantly to the overall prediction of text reading fluency. The model prediction was higher (from ca. 37 to 52% of the explained variance) when we included the naming of discrete digits variable, which had a suppressive effect on pseudo-word reading. In the dyslexic readers, the variance explained by the two-factor model was high (69%) and did not change when the third factor was added. The lack of a suppression effect was likely due to the prominent individual differences in poor orthographic decoding of the dyslexic children. Analyses on data from both groups of children were replicated by using patches of colors as stimuli (both in the RAN task and in the discrete naming task) obtaining similar results. We conclude that it is possible to predict much of the variance in text-reading fluency using basic processes, such as orthographic decoding and integration of reading sub-components, even without taking into consideration higher-order linguistic factors such as lexical, semantic and contextual abilities. The approach validity of using proximal vs. distal causes to predict reading fluency is discussed.

Dataset Information

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

Publications

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

Similar Datasets

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