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Nuttakorn Thubthong - One of the best experts on this subject based on the ideXlab platform.
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Recognition rate prediction for dysarthric speech disorder via speech consistency score
Lecture Notes in Computer Science, 2006Co-Authors: Prakasith Kayasith, Thanaruk Theeramunkong, Nuttakorn ThubthongAbstract:Dysarthria is a collection of motor speech disorder. A severity of Dysarthria is traditionally evaluated by human expertise or a group of listener. This paper proposes a new indicator called speech consistency score (SCS). By considering the relation of speech similarity-dissimilarity, SCS can be applied to evaluate the severity of dysarthric speaker. Aside from being used as a tool for speech assessment, SCS can be used to predict the possible outcome of speech recognition as well. A number of experiments are made to compare predicted recognition rates, generated by SCS, with the recognition rates of two well-known recognition systems, HMM and ANN. The result shows that the root mean square error between the prediction rates and recognition rates are less than 7.0% (R 2 = 0.74) and 2.5% (R 2 = 0.96) for HMM and ANN, respectively. Moreover, to utilized the use of SCS in general case, the test on unknown recognition set showed the error of 11 % (R 2 = 0.48) for HMM.
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PRICAI - Recognition rate prediction for dysarthric speech disorder via speech consistency score
Lecture Notes in Computer Science, 2006Co-Authors: Prakasith Kayasith, Thanaruk Theeramunkong, Nuttakorn ThubthongAbstract:Dysarthria is a collection of motor speech disorder. A severity of Dysarthria is traditionally evaluated by human expertise or a group of listener. This paper proposes a new indicator called speech consistency score (SCS). By considering the relation of speech similarity-dissimilarity, SCS can be applied to evaluate the severity of dysarthric speaker. Aside from being used as a tool for speech assessment, SCS can be used to predict the possible outcome of speech recognition as well. A number of experiments are made to compare predicted recognition rates, generated by SCS, with the recognition rates of two well-known recognition systems, HMM and ANN. The result shows that the root mean square error between the prediction rates and recognition rates are less than 7.0% (R2 = 0.74) and 2.5% (R2 = 0.96) for HMM and ANN, respectively. Moreover, to utilized the use of SCS in general case, the test on unknown recognition set showed the error of 11% (R2 = 0.48) for HMM.
Prakasith Kayasith - One of the best experts on this subject based on the ideXlab platform.
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Speech Clarity Index (ψ): A Distance-Based Speech Quality Indicator and Recognition Rate Prediction for Dysarthric Speakers with Cerebral Palsy
IEICE Transactions on Information and Systems, 2009Co-Authors: Prakasith Kayasith, Thanaruk TheeramunkongAbstract:It is a tedious and subjective task to measure severity of a Dysarthria by manually evaluating his/her speech using available standard assessment methods based on human perception. This paper presents an automated approach to assess speech quality of a dysarthric speaker with cerebral palsy. With the consideration of two complementary factors, speech consistency and speech distinction, a speech quality indicator called speech clarity index (ψ) is proposed as a measure of the speaker's ability to produce consistent speech signal for a certain word and distinguished speech signal for different words. As an application, it can be used to assess speech quality and forecast speech recognition rate of speech made by an individual dysarthric speaker before actual exhaustive implementation of an automatic speech recognition system for the speaker. The effectiveness of ψ as a speech recognition rate predictor is evaluated by rank-order inconsistency, correlation coefficient, and root-mean-square of difference. The evaluations had been done by comparing its predicted recognition rates with ones predicted by the standard methods called the articulatory and intelligibility tests based on the two recognition systems (HMM and ANN). The results show that ψ is a promising indicator for predicting recognition rate of dysarthric speech. All experiments had been done on speech corpus composed of speech data from eight normal speakers and eight dysarthric speakers.
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Recognition rate prediction for dysarthric speech disorder via speech consistency score
Lecture Notes in Computer Science, 2006Co-Authors: Prakasith Kayasith, Thanaruk Theeramunkong, Nuttakorn ThubthongAbstract:Dysarthria is a collection of motor speech disorder. A severity of Dysarthria is traditionally evaluated by human expertise or a group of listener. This paper proposes a new indicator called speech consistency score (SCS). By considering the relation of speech similarity-dissimilarity, SCS can be applied to evaluate the severity of dysarthric speaker. Aside from being used as a tool for speech assessment, SCS can be used to predict the possible outcome of speech recognition as well. A number of experiments are made to compare predicted recognition rates, generated by SCS, with the recognition rates of two well-known recognition systems, HMM and ANN. The result shows that the root mean square error between the prediction rates and recognition rates are less than 7.0% (R 2 = 0.74) and 2.5% (R 2 = 0.96) for HMM and ANN, respectively. Moreover, to utilized the use of SCS in general case, the test on unknown recognition set showed the error of 11 % (R 2 = 0.48) for HMM.
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PRICAI - Recognition rate prediction for dysarthric speech disorder via speech consistency score
Lecture Notes in Computer Science, 2006Co-Authors: Prakasith Kayasith, Thanaruk Theeramunkong, Nuttakorn ThubthongAbstract:Dysarthria is a collection of motor speech disorder. A severity of Dysarthria is traditionally evaluated by human expertise or a group of listener. This paper proposes a new indicator called speech consistency score (SCS). By considering the relation of speech similarity-dissimilarity, SCS can be applied to evaluate the severity of dysarthric speaker. Aside from being used as a tool for speech assessment, SCS can be used to predict the possible outcome of speech recognition as well. A number of experiments are made to compare predicted recognition rates, generated by SCS, with the recognition rates of two well-known recognition systems, HMM and ANN. The result shows that the root mean square error between the prediction rates and recognition rates are less than 7.0% (R2 = 0.74) and 2.5% (R2 = 0.96) for HMM and ANN, respectively. Moreover, to utilized the use of SCS in general case, the test on unknown recognition set showed the error of 11% (R2 = 0.48) for HMM.
Thanaruk Theeramunkong - One of the best experts on this subject based on the ideXlab platform.
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Speech Clarity Index (ψ): A Distance-Based Speech Quality Indicator and Recognition Rate Prediction for Dysarthric Speakers with Cerebral Palsy
IEICE Transactions on Information and Systems, 2009Co-Authors: Prakasith Kayasith, Thanaruk TheeramunkongAbstract:It is a tedious and subjective task to measure severity of a Dysarthria by manually evaluating his/her speech using available standard assessment methods based on human perception. This paper presents an automated approach to assess speech quality of a dysarthric speaker with cerebral palsy. With the consideration of two complementary factors, speech consistency and speech distinction, a speech quality indicator called speech clarity index (ψ) is proposed as a measure of the speaker's ability to produce consistent speech signal for a certain word and distinguished speech signal for different words. As an application, it can be used to assess speech quality and forecast speech recognition rate of speech made by an individual dysarthric speaker before actual exhaustive implementation of an automatic speech recognition system for the speaker. The effectiveness of ψ as a speech recognition rate predictor is evaluated by rank-order inconsistency, correlation coefficient, and root-mean-square of difference. The evaluations had been done by comparing its predicted recognition rates with ones predicted by the standard methods called the articulatory and intelligibility tests based on the two recognition systems (HMM and ANN). The results show that ψ is a promising indicator for predicting recognition rate of dysarthric speech. All experiments had been done on speech corpus composed of speech data from eight normal speakers and eight dysarthric speakers.
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Recognition rate prediction for dysarthric speech disorder via speech consistency score
Lecture Notes in Computer Science, 2006Co-Authors: Prakasith Kayasith, Thanaruk Theeramunkong, Nuttakorn ThubthongAbstract:Dysarthria is a collection of motor speech disorder. A severity of Dysarthria is traditionally evaluated by human expertise or a group of listener. This paper proposes a new indicator called speech consistency score (SCS). By considering the relation of speech similarity-dissimilarity, SCS can be applied to evaluate the severity of dysarthric speaker. Aside from being used as a tool for speech assessment, SCS can be used to predict the possible outcome of speech recognition as well. A number of experiments are made to compare predicted recognition rates, generated by SCS, with the recognition rates of two well-known recognition systems, HMM and ANN. The result shows that the root mean square error between the prediction rates and recognition rates are less than 7.0% (R 2 = 0.74) and 2.5% (R 2 = 0.96) for HMM and ANN, respectively. Moreover, to utilized the use of SCS in general case, the test on unknown recognition set showed the error of 11 % (R 2 = 0.48) for HMM.
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PRICAI - Recognition rate prediction for dysarthric speech disorder via speech consistency score
Lecture Notes in Computer Science, 2006Co-Authors: Prakasith Kayasith, Thanaruk Theeramunkong, Nuttakorn ThubthongAbstract:Dysarthria is a collection of motor speech disorder. A severity of Dysarthria is traditionally evaluated by human expertise or a group of listener. This paper proposes a new indicator called speech consistency score (SCS). By considering the relation of speech similarity-dissimilarity, SCS can be applied to evaluate the severity of dysarthric speaker. Aside from being used as a tool for speech assessment, SCS can be used to predict the possible outcome of speech recognition as well. A number of experiments are made to compare predicted recognition rates, generated by SCS, with the recognition rates of two well-known recognition systems, HMM and ANN. The result shows that the root mean square error between the prediction rates and recognition rates are less than 7.0% (R2 = 0.74) and 2.5% (R2 = 0.96) for HMM and ANN, respectively. Moreover, to utilized the use of SCS in general case, the test on unknown recognition set showed the error of 11% (R2 = 0.48) for HMM.
Alku Paavo - One of the best experts on this subject based on the ideXlab platform.
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Duration of the rhotic approximant /ô/ in spastic Dysarthria of different severity levels
'Elsevier BV', 2023Co-Authors: Gurugubelli Krishna, Vuppala Anil, Nonavinakere Prabhakera Narendra, Alku PaavoAbstract:Dysarthria is a motor speech disorder leading to imprecise articulation of speech. Acoustic analysis capable of detecting and assessing articulation errors is useful in Dysarthria diagnosis and therapy. Since speakers with Dysarthria experience difficulty in producing rhotics due to complex articulatory gestures of these sounds, the hypothesis of the present study is that duration of the rhotic approximant /ô/ distinguishes dysarthric speech of different severity levels. Duration measurements were conducted using the third formant (F3) trajectories estimated from quasi-closed-phase (QCP) spectrograms. Results indicate that the severity level of spastic Dysarthria has a significant effect on duration of /ô/. In addition, the phonetic context has a significant effect on duration of /ô/, the I-r-E context showing the largest difference in /ô/ duration between dysarthric speech of the highest severity levels and healthy speech. The results of this preliminary study can be used in the future to develop signal processing and machine learning methods to automatically predict the severity level of spastic Dysarthria from speech signals.Peer reviewe
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Automatic assessment of intelligibility in speakers with Dysarthria from coded telephone speech using glottal features
'Elsevier BV', 2022Co-Authors: Nonavinakere Prabhakera Narendra, Alku PaavoAbstract:In clinical practice, assessment of intelligibility in speakers with Dysarthria is performed by speech-language pathologists through auditory perceptual tests which demand patients’ presence at hospital and involve time-consuming examinations. Frequent clinical monitoring can be costly and logistically inconvenient both for patients and medical experts. Here, we aim to automate the procedure of assessment of intelligibility in dysarthric speakers with an objective, speech-based method that can be employed in a telescreening application. The proposed method predicts the level of intelligibility in dysarthric speakers using four levels of speech intelligibility (very low, low, mediocre and high). The study compares several automatic methods to assess the intelligibility level in speakers with Dysarthria by utilizing information generated at the level of the vocal folds through glottal features and by using coded telephone speech (i.e. speech that is used in telescreening applications). In addition to the glottal features, the openS-MILE features are used as acoustic baseline features. Using features obtained from coded speech utterances and the corresponding intelligibility level labels, multiclass-support vector machine (SVM) classifiers are trained. A separate set of multiclass-SVMs are trained using both individual glottal and acoustic features as well as their combinations. Coded telephone speech is generated with the adaptive multi-rate codec with two operational bandwidths (narrowband and wideband), from utterances of an open database of dysarthric speech (Universal Access-Speech). Experimental results showed good classification accuracies for the glottal features, indicating their effectiveness in the intelligibility level assessment in speakers with Dysarthria even in the challenging coded condi-tion. Improvement in classification accuracy was obtained when the glottal features were combined with the openSMILE acoustic features, which validate the complimentary nature of the glottal features.Peer reviewe
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Automatic intelligibility assessment of dysarthric speech using glottal parameters
'Elsevier BV', 2022Co-Authors: Nonavinakere Prabhakera Narendra, Alku PaavoAbstract:Objective intelligibility assessment of dysarthric speech can assist clinicians in diagnosis of speech disorders as well as in medical treatment. This study investigates the use of glottal parameters (i.e. parameters that describe the acoustical excitation of voiced speech, the glottal flow) in the automatic intelligibility assessment of dysarthric speech. Instead of directly predicting the intelligibility of dysarthric speech using a single-stage system, the proposed method utilizes a two-stage framework. In the first stage, two-class severity classification of Dysarthria is performed using support vector machines (SVMs). In the second stage, intelligibility estimation of dysarthric speech is computed using a linear regression model. Two sets of glottal parameters are explored: (1) time-domain and frequency-domain parameters and (2) parameters based on principal component analysis (PCA).Acoustic parameters proposed in a similar intelligibility prediction study by Falk et al. [1] are used as baseline features. Evaluation results show that the two-stage framework leads to improvement in the intelligibility assessment measures (correlation and root mean square error) compared to the single-stage framework. The combination of the glottal parameters sets results in better performance in the severity classification and intelligibility estimation tasks compared to the baseline features.Peer reviewe
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Dysarthric speech classification from coded telephone speech using glottal features
'Elsevier BV', 2021Co-Authors: Nonavinakere Prabhakera Narendra, Alku PaavoAbstract:This paper proposes a new dysarthric speech classification method from coded telephone speech using glottal features. The proposed method utilizes glottal features, which are efficiently estimated from coded telephone speech using a recently proposed deep neural net-based glottal inverse filtering method. Two sets of glottal features were considered: (1) time- and frequency-domain parameters and (2) parameters based on principal component analysis (PCA). In addition, acoustic features are extracted from coded telephone speech using the openSMILE toolkit. The proposed method utilizes both acoustic and glottal features extracted from coded speech utterances and their corresponding dysarthric/healthy labels to train support vector machine classifiers. Separate classifiers are trained using both individual, and the combination of glottal and acoustic features. The coded telephone speech used in the experiments is generated using the adaptive multi-rate codec, which operates in two transmission bandwidths: narrowband (300 Hz - 3.4 kHz) and wideband (50 Hz - 7 kHz). The experiments were conducted using dysarthric and healthy speech utterances of the TORGO and universal access speech (UA-Speech) databases. Classification accuracy results indicated the effectiveness of glottal features in the identification of Dysarthria from coded telephone speech. The results also showed that the glottal features in combination with the openSMILE-based acoustic features resulted in improved classification accuracies, which validate the complementary nature of glottal features. The proposed dysarthric speech classification method can potentially be employed in telemonitoring application for identifying the presence of Dysarthria from coded telephone speech.Peer reviewe
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Dysarthric speech classification from coded telephone speech using glottal features
'Elsevier BV', 2021Co-Authors: Nonavinakere Prabhakera Narendra, Alku PaavoAbstract:This paper proposes a new dysarthric speech classification method from coded telephone speech using glottal features. The proposed method utilizes glottal features, which are efficiently estimated from coded telephone speech using a recently proposed deep neural net-based glottal inverse filtering method. Two sets of glottal features were considered: (1) time- and frequency-domain parameters and (2) parameters based on principal component analysis (PCA). In addition, acoustic features are extracted from coded telephone speech using the openSMILE toolkit. The proposed method utilizes both acoustic and glottal features extracted from coded speech utterances and their corresponding dysarthric/healthy labels to train support vector machine classifiers. Separate classifiers are trained using both individual, and the combination of glottal and acoustic features. The coded telephone speech used in the experiments is generated using the adaptive multi-rate codec, which operates in two transmission bandwidths: narrowband (300 Hz - 3.4 kHz) and wideband (50 Hz - 7 kHz). The experiments were conducted using dysarthric and healthy speech utterances of the TORGO and universal access speech (UA-Speech) databases. Classification accuracy results indicated the effectiveness of glottal features in the identification of Dysarthria from coded telephone speech. The results also showed that the glottal features in combination with the openSMILE-based acoustic features resulted in improved classification accuracies, which validate the complementary nature of glottal features. The proposed dysarthric speech classification method can potentially be employed in telemonitoring application for identifying the presence of Dysarthria from coded telephone speech.Peer reviewe
Kathryn M. Yorkston - One of the best experts on this subject based on the ideXlab platform.
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The Degenerative Dysarthrias: A Window into Critical Clinical and Research Issues
Folia phoniatrica et logopaedica : official organ of the International Association of Logopedics and Phoniatrics (IALP), 2007Co-Authors: Kathryn M. YorkstonAbstract:Although diversity of symptoms and urgency of needs pose many challenges, management of the degenerative Dysarthrias is a crucial aspect of clinical practice. The purpose of this article is to review current research literature on selected degenerative Dysarthrias including those associated with Parkinson's disease, multiple sclerosis, and amyotrophic lateral sclerosis. These Dysarthrias are prevalent yet represent distinct patterns of underlying neuropathology, symptoms, age of onset, and rate of progression. Literature searches including the period 1997-2006 yielded 148 different studies reporting data on communication issues related to Dysarthria. By far the largest category of studies was that which provided a basic description of speech production including the neurophysiologic, acoustic, or perceptual properties of Dysarthria. Other categories included management (assessment and treatment) and the psychosocial consequences of Dysarthria. While the topic of management of degenerative Dysarthria is a focused one, it provides a window into many issues critical to the field of communication disorders including fundamental properties of speech production, development of evidence-based treatment techniques, the staging of these techniques into an effective management sequence, and the psychosocial consequences of communication disorders along with techniques to maintain communicative participation in the face of degenerative conditions.
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Treatment efficacy: Dysarthria
Journal of speech and hearing research, 1996Co-Authors: Kathryn M. YorkstonAbstract:The Dysarthrias form a group of diverse, chronic motor speech disorders. The disorders of Parkinson's disease, stroke, traumatic brain injury, amyotrophic lateral sclerosis, and cerebral palsy are reviewed because they represent important clinical diagnoses in which Dysarthria is a frequent and debilitating symptom. The roles played by speech-language pathologists include participation in differential diagnosis, provision of speech treatment, staging of treatment, and timely education so that clients and families can make informed decisions about communication alternatives. Both scientific and clinical evidence is presented that suggests that individuals with Dysarthria benefit from the services of speech-language pathologists. Group-treatment studies, single-subject studies, and case reports illustrate the effectiveness of various types of speech treatment. Research into the effectiveness of augmentative and alternative communication systems for individuals with cerebral palsy is also presented.