The Experts below are selected from a list of 6222 Experts worldwide ranked by ideXlab platform
Visar Berisha - One of the best experts on this subject based on the ideXlab platform.
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robust estimation of hypernasality in dysarthria with acoustic model likelihood features
IEEE Transactions on Audio Speech and Language Processing, 2020Co-Authors: Michael Saxon, Julie M Liss, Ayush Tripathi, Yishan Jiao, Visar BerishaAbstract:Hypernasality is a common characteristic symptom across many Motor-Speech Disorders. For voiced sounds, hypernasality introduces an additional resonance in the lower frequencies and, for unvoiced sounds, there is reduced articulatory precision due to air escaping through the nasal cavity. However, the acoustic manifestation of these symptoms is highly variable, making hypernasality estimation very challenging, both for human specialists and automated systems. Previous work in this area relies on either engineered features based on statistical signal processing or machine learning models trained on clinical ratings. Engineered features often fail to capture the complex acoustic patterns associated with hypernasality, whereas metrics based on machine learning are prone to overfitting to the small disease-specific Speech datasets on which they are trained. Here we propose a new set of acoustic features that capture these complementary dimensions. The features are based on two acoustic models trained on a large corpus of healthy Speech. The first acoustic model aims to measure nasal resonance from voiced sounds, whereas the second acoustic model aims to measure articulatory imprecision from unvoiced sounds. To demonstrate that the features derived from these acoustic models are specific to hypernasal Speech, we evaluate them across different dysarthria corpora. Our results show that the features generalize even when training on hypernasal Speech from one disease and evaluating on hypernasal Speech from another disease (e.g., training on Parkinson's disease, evaluation on Huntington's disease), and when training on neurologically disordered Speech but evaluating on cleft palate Speech.
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robust estimation of hypernasality in dysarthria
arXiv: Audio and Speech Processing, 2019Co-Authors: Michael Saxon, Julie M Liss, Ayush Tripathi, Yishan Jiao, Visar BerishaAbstract:Hypernasality is a common symptom across many Motor-Speech Disorders. For voiced sounds, hypernasality introduces an additional resonance in the lower frequencies and, for unvoiced sounds, there is reduced articulatory precision due to air escaping through the nasal cavity. However, the acoustic manifestation of these symptoms is highly variable, making hypernasality estimation very challenging, both for human specialists and automated systems. Previous work in this area relies on either engineered features based on statistical signal processing or machine learning models trained on clinical ratings. Engineered features often fail to capture the complex acoustic patterns associated with hypernasality, whereas metrics based on machine learning are prone to overfitting to the small disease-specific Speech datasets on which they are trained. Here we propose a new set of acoustic features that capture these complementary dimensions. The features are based on two acoustic models trained on a large corpus of healthy Speech. The first acoustic model aims to measure nasal resonance from voiced sounds, whereas the second acoustic model aims to measure articulatory imprecision from unvoiced sounds. To demonstrate that the features derived from these acoustic models are specific to hypernasal Speech, we evaluate them across different dysarthria corpora. Our results show that the features generalize even when training on hypernasal Speech from one disease and evaluating on hypernasal Speech from another disease (e.g. training on Parkinson's disease, evaluation on Huntington's disease).
Karolina Janacsek - One of the best experts on this subject based on the ideXlab platform.
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the neurocognition of developmental Disorders of language
Annual Review of Psychology, 2020Co-Authors: Michael T Ullman, Sayako F Earle, Matthew Walenski, Karolina JanacsekAbstract:Developmental Disorders of language include developmental language disorder, dyslexia, and Motor-Speech Disorders such as articulation disorder and stuttering. These Disorders have generally been e...
Michael Saxon - One of the best experts on this subject based on the ideXlab platform.
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robust estimation of hypernasality in dysarthria with acoustic model likelihood features
IEEE Transactions on Audio Speech and Language Processing, 2020Co-Authors: Michael Saxon, Julie M Liss, Ayush Tripathi, Yishan Jiao, Visar BerishaAbstract:Hypernasality is a common characteristic symptom across many Motor-Speech Disorders. For voiced sounds, hypernasality introduces an additional resonance in the lower frequencies and, for unvoiced sounds, there is reduced articulatory precision due to air escaping through the nasal cavity. However, the acoustic manifestation of these symptoms is highly variable, making hypernasality estimation very challenging, both for human specialists and automated systems. Previous work in this area relies on either engineered features based on statistical signal processing or machine learning models trained on clinical ratings. Engineered features often fail to capture the complex acoustic patterns associated with hypernasality, whereas metrics based on machine learning are prone to overfitting to the small disease-specific Speech datasets on which they are trained. Here we propose a new set of acoustic features that capture these complementary dimensions. The features are based on two acoustic models trained on a large corpus of healthy Speech. The first acoustic model aims to measure nasal resonance from voiced sounds, whereas the second acoustic model aims to measure articulatory imprecision from unvoiced sounds. To demonstrate that the features derived from these acoustic models are specific to hypernasal Speech, we evaluate them across different dysarthria corpora. Our results show that the features generalize even when training on hypernasal Speech from one disease and evaluating on hypernasal Speech from another disease (e.g., training on Parkinson's disease, evaluation on Huntington's disease), and when training on neurologically disordered Speech but evaluating on cleft palate Speech.
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robust estimation of hypernasality in dysarthria
arXiv: Audio and Speech Processing, 2019Co-Authors: Michael Saxon, Julie M Liss, Ayush Tripathi, Yishan Jiao, Visar BerishaAbstract:Hypernasality is a common symptom across many Motor-Speech Disorders. For voiced sounds, hypernasality introduces an additional resonance in the lower frequencies and, for unvoiced sounds, there is reduced articulatory precision due to air escaping through the nasal cavity. However, the acoustic manifestation of these symptoms is highly variable, making hypernasality estimation very challenging, both for human specialists and automated systems. Previous work in this area relies on either engineered features based on statistical signal processing or machine learning models trained on clinical ratings. Engineered features often fail to capture the complex acoustic patterns associated with hypernasality, whereas metrics based on machine learning are prone to overfitting to the small disease-specific Speech datasets on which they are trained. Here we propose a new set of acoustic features that capture these complementary dimensions. The features are based on two acoustic models trained on a large corpus of healthy Speech. The first acoustic model aims to measure nasal resonance from voiced sounds, whereas the second acoustic model aims to measure articulatory imprecision from unvoiced sounds. To demonstrate that the features derived from these acoustic models are specific to hypernasal Speech, we evaluate them across different dysarthria corpora. Our results show that the features generalize even when training on hypernasal Speech from one disease and evaluating on hypernasal Speech from another disease (e.g. training on Parkinson's disease, evaluation on Huntington's disease).
Hayo Terband - One of the best experts on this subject based on the ideXlab platform.
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auditory Motor interactions in pediatric Motor Speech Disorders neurocomputational modeling of disordered development
Journal of Communication Disorders, 2014Co-Authors: Hayo Terband, Ben Maassen, Frank H Guenther, Jonathan S BrumbergAbstract:BACKGROUND/PURPOSE: Differentiating the symptom complex due to phonological-level Disorders, Speech delay and pediatric Motor Speech Disorders is a controversial issue in the field of pediatric Speech and language pathology. The present study investigated the developmental interaction between neurological deficits in auditory and Motor processes using computational modeling with the DIVA model. METHOD: In a series of computer simulations, we investigated the effect of a Motor processing deficit alone (MPD), and the effect of a Motor processing deficit in combination with an auditory processing deficit (MPD+APD) on the trajectory and endpoint of Speech Motor development in the DIVA model. RESULTS: Simulation results showed that a Motor programming deficit predominantly leads to deterioration on the phonological level (phonemic mappings) when auditory self-monitoring is intact, and on the systemic level (systemic mapping) if auditory self-monitoring is impaired. CONCLUSIONS: These findings suggest a close relation between quality of auditory self-monitoring and the involvement of phonological vs. Motor processes in children with pediatric Motor Speech Disorders. It is suggested that MPD+APD might be involved in typically apraxic Speech output Disorders and MPD in pediatric Motor Speech Disorders that also have a phonological component. Possibilities to verify these hypotheses using empirical data collected from human subjects are discussed. Learning outcomes: The reader will be able to: (1) identify the difficulties in studying disordered Speech Motor development; (2) describe the differences in Speech Motor characteristics between SSD and subtype CAS; (3) describe the different types of learning that occur in the sensory–Motor system during babbling and early Speech acquisition; (4) identify the neural control subsystems involved in Speech production; (5) describe the potential role of auditory self-monitoring in developmental Speech Disorders. (PsycINFO Database Record (c) 2014 APA, all rights reserved) (journal abstract)
Herve Bourlard - One of the best experts on this subject based on the ideXlab platform.
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automatic and perceptual discrimination between dysarthria apraxia of Speech and neurotypical Speech
International Conference on Acoustics Speech and Signal Processing, 2021Co-Authors: Ina Kodrasi, Michaela Pernon, Marina Laganaro, Herve BourlardAbstract:Automatic techniques in the context of Motor Speech Disorders (MSDs) are typically two-class techniques aiming to discriminate between dysarthria and neurotypical Speech or between dysarthria and apraxia of Speech (AoS). Further, although such techniques are proposed to support the perceptual assessment of clinicians, the automatic and perceptual classification accuracy has never been compared. In this paper, we investigate a three-class automatic technique and a set of handcrafted features for the discrimination of dysarthria, AoS and neurotypical Speech. Instead of following the commonly used One-versus-One or One-versus-Rest approaches for multi-class classification, a hierarchical approach is proposed. Further, a perceptual study is conducted where Speech and language pathologists are asked to listen to recordings of dysarthria, AoS, and neurotypical Speech and decide which class the recordings belong to. The proposed automatic technique is evaluated on the same recordings and the automatic and perceptual classification performance are compared. The presented results show that the hierarchical classification approach yields a higher classification accuracy than baseline One-versus-One and One-versus-Rest approaches. Further, the presented results show that the automatic approach yields a higher classification accuracy than the perceptual assessment of Speech and language pathologists, demonstrating the potential advantages of integrating automatic tools in clinical practice.