Extending Attention Span for Children ADHD Using an Attentive Visual Interface

Asiry, Othman; Shen, Haifeng; Wyeld, Theodor; Balkhy, Soher

doi:10.1109/iv.2018.00041

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…In other words, the ability of comprehending logic and sequential series of events must have been, to a certain degree, built upon the primal ability of recognizing sensory data, one of which is speech. For example, increasing the contrast of sensory data may extend the attention spans according to Asiry et al (2018). Accordingly, the resolution of the speech features can affect the word recognition quality, which can affect the grammar recognition quality.…”

Section: Discussionmentioning

confidence: 99%

Speech Quefrency Transform (SQT)

Hasanain

Këpuska

Silaghi

et al. 2022

Preprint

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The fundamental frequency feature is crucial for Automatic Speech Recognition because its patterns convey a para-language and its tuning normalizes other speech features. Human speech is multidimensional because it is minimally represented by three variables: the intonation (or pitch), the formants (or timbre), and the speech resolution (or depth). These variables represent the hidden states of the local glottal variation, the vocal tract response, and the frequency scale, respectively. Computing them one by one is not as efficient as computing them together, so this article introduces a new speech feature extraction approach. The article is introductory; it focuses on the basic concepts of our new approach and does not elaborate on all applications. It demonstrates that the unit of a cepstral value, which is a spectral value of spectrums, is a unit of acceleration since its discrete variable, the quefrency, can be expressed in Hertz-per-microsecond. The article shows how to produce refined voice analysis from robust estimates and how to reconstruct speech signals from feature spaces. And it concludes that the pitch track of the new approach is as good as two open-source pitch extractors. Combining multiple processes, attenuating background noises, and enabling distant-speech recognition, we introduce the Speech Quefrency Transform (SQT) approach as well as multiple quefrency scales. SQT is a set of frequency transforms whose spectral leakages are controlled per a frequency-modulation model. SQT captures the stationarity of time series onto a hyperspace that resembles the cepstrogram when it is reduced for pitch track extraction.

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Section: Discussionmentioning

confidence: 99%

Speech Quefrency Transform (SQT)

Hasanain

Këpuska

Silaghi

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In other words, the ability of comprehending logic and sequential series of events must have been, to a certain degree, built upon the primal ability of recognizing sensory data, one of which is speech. For example, increasing the contrast of sensory data may extend the attention spans according to Asiry et al [44]. Accordingly, the resolution of the speech features can affect the word recognition quality, which can affect the grammar recognition quality.…”

Section: Discussionmentioning

confidence: 99%

GIS and Remote Sensing Based Land Degradation Assessment and Mapping: Case Study Adea Woreda

2022

JEEE

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The fundamental frequency feature is essential for Automatic Speech Recognition because its patterns convey a paralanguage and its tuning normalizes other speech features. Human speech is multidimensional because it is minimally represented by three variables: the intonation (or pitch), the formants (or timbre), and the speech resolution (or depth). These variables represent the hidden states of the local glottal variation, the vocal tract response, and the frequency scale, respectively. Computing them one by one is not as efficient as computing them together, so this article introduces a new speech feature extraction approach. The article is introductory; it focuses on the basic concepts of our new approach and does not elaborate on all applications. It demonstrates that the unit of a cepstral value, which is a spectral value of spectrums, is a unit of acceleration since its discrete variable, the quefrency, can be expressed in Hertz-per-microsecond. The article shows how to produce refined voice analysis from robust estimates and how to reconstruct speech signals from feature spaces. And it concludes that the pitch track of the new approach is as good as two open-source pitch extractors. Combining multiple processes, attenuating background noises, and enabling distant-speech recognition, we introduce the Speech Quefrency Transform (SQT) approach as well as multiple quefrency scales. SQT is a set of frequency transforms whose spectral leakages are controlled per a frequency-modulation model. SQT captures the stationarity of time series onto a hyperspace that resembles the cepstrogram when it is reduced for pitch track extraction.

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“…Constituting a self-aware agent, hierarchical spans of language are levels for intelligence. Increasing the contrast of visualization can extend the attention spans hindered due to developmental disorders Asiry et al (2018). Additionally, magnetic resonance imaging (MRI) has revealed that both the processing of language and the ability of using tools stimulate similar neural areas Higuchi et al (2009).…”

Section: Spans Of Language and Intelligencementioning

confidence: 99%

Speech Quefrency Transform (SQT)

Hasanain

Këpuska

Silaghi

et al. 2022

Preprint

View full text Add to dashboard Cite

Human speech consists mainly of three components: a glottal signal, a vocal tract response, and a harmonic shift. The three respectively correlate with the intonation (pitch), the formants (timbre), and the speech resolution (depth). Adding the intonation of the Fundamental Frequency (FF) to Automatic Speech Recognition (ASR) systems is necessary. First, the intonation conveys a primitive para-language. Second, its speaker-tuning reduces background noises to clarify acoustic observations. Third, extracting the speech features is more efficient when they are computed together at the same time.This work introduces a frequency-modulation model, a novel quefrency-based speech features' extraction that is named Speech Quefrency Transform (SQT), and its proper quefrency scaling and transformation function. The cepstrums, which are spectrums of spectrums, are suggested in time unit accelerations, whereby the discrete variable, the quefrency, is measured in Hertz-per-microsecond. The extracted features are comparable to Mel-Frequency Cepstral Coefficients (MFCC) integrated within a quefrency-based pitch tracker. The SQT transform directly expands time samples of stationary signals (i.e., speech) to a higher dimensional space, which can help generative Artificial Neural Networks (ANNs) in unsupervised Machine Learning and Natural Language Processing (NLP) tasks. The proposed methodologies, which are a scalable solution that is compatible with dynamic and parallel programming for refined speech and cepstral analysis, can robustly estimate the features after applying a matrix multiplication in less than a hundred sub-bands, preserving precious computational resources.

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Extending Attention Span for Children ADHD Using an Attentive Visual Interface

Cited by 6 publications

References 24 publications

Speech Quefrency Transform (SQT)

Speech Quefrency Transform (SQT)

GIS and Remote Sensing Based Land Degradation Assessment and Mapping: Case Study Adea Woreda

Speech Quefrency Transform (SQT)

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