The Experts below are selected from a list of 1509 Experts worldwide ranked by ideXlab platform
Yongsoo Kim - One of the best experts on this subject based on the ideXlab platform.
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investigation of postoperative Hypernasality after superiorly based posterior pharyngeal flap
Maxillofacial plastic and reconstructive surgery, 2018Co-Authors: Yu-jeong Shin, Yongsoo KimAbstract:Velopharyngeal insufficiency that accompanies speech resonance and articulation disorders can be managed through several intervention methods such as speech-language therapy, prosthetic aids, and surgery. However, for patients with severe Hypernasality, surgical interventions are highly recommended. Among available surgical techniques, the posterior pharyngeal flap is most common. Two adult males with high nasalance scores underwent superiorly based posterior pharyngeal flap surgery, followed by speech testing by an expert speech-language therapist. Nasalance scores and articulation accuracy were assessed up until 1 year after the surgery. Nasalance scores were measured five times using a nasometer, after which the average value was calculated. Consistent declines in Hypernasality over time are not easy to explain since the pedicled pharyngeal flap narrowed over time, secondary to cicatrization. However, scar tethering of the soft palate in a posterior direction could reduce the velopharyngeal port size over time. Therefore, long-term follow-up with intensive speech therapy is suggested for patients with severe Hypernasality.
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Investigation of postoperative Hypernasality after superiorly based posterior pharyngeal flap
SpringerOpen, 2018Co-Authors: Yu-jeong Shin, Yongsoo KimAbstract:Abstract Background Velopharyngeal insufficiency that accompanies speech resonance and articulation disorders can be managed through several intervention methods such as speech-language therapy, prosthetic aids, and surgery. However, for patients with severe Hypernasality, surgical interventions are highly recommended. Among available surgical techniques, the posterior pharyngeal flap is most common. Case presentation Two adult males with high nasalance scores underwent superiorly based posterior pharyngeal flap surgery, followed by speech testing by an expert speech-language therapist. Nasalance scores and articulation accuracy were assessed up until 1 year after the surgery. Nasalance scores were measured five times using a nasometer, after which the average value was calculated. Conclusions Consistent declines in Hypernasality over time are not easy to explain since the pedicled pharyngeal flap narrowed over time, secondary to cicatrization. However, scar tethering of the soft palate in a posterior direction could reduce the velopharyngeal port size over time. Therefore, long-term follow-up with intensive speech therapy is suggested for patients with severe Hypernasality
David P Kuehn - One of the best experts on this subject based on the ideXlab platform.
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automatic identification of Hypernasality in normal and cleft lip and palate patients with acoustic analysis of speech
Journal of the Acoustical Society of America, 2017Co-Authors: Marzieh Golabbakhsh, Fatemeh Khanlar, Fatemeh Derakhshandeh, Fatemeh Abnavi, Mina Kadkhodaei Elyaderani, Panying Rong, David P KuehnAbstract:Hypernasality is seen in cleft lip and palate patients who had undergone repair surgery as a consequence of velopharyngeal insufficiency. Hypernasality has been studied by evaluation of perturbation, noise measures, and cepstral analysis of speech. In this study, feature extraction and analysis were performed during running speech using six different sentences. Jitter, shimmer, Mel frequency cepstral coefficients, bionic wavelet transform entropy, and bionic wavelet transform energy were calculated. Support vector machines were employed for classification of data to normal or hypernasal. Finally, results of the automatic classification were compared with true labels to find accuracy, sensitivity, and specificity. Accuracy was higher when Mel frequency cepstral coefficients were combined with bionic wavelet transform energy feature. In the best case, accuracy of 85% with sensitivity of 82% and specificity of 85% was obtained. Results prove that acoustic analysis is a reliable method to find Hypernasality in cleft lip and palate patients.
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the effect of articulatory adjustment on reducing Hypernasality
Journal of Speech Language and Hearing Research, 2012Co-Authors: Panying Rong, David P KuehnAbstract:Purpose With the goal of using articulatory adjustments to reduce Hypernasality, this study utilized an articulatory synthesis model (Childers, 2000) to simulate the adjustment of articulatory conf...
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efficacy of continuous positive airway pressure for treatment of Hypernasality
The Cleft Palate-Craniofacial Journal, 2002Co-Authors: David P Kuehn, D R Van Demark, Peter B Imrey, Lucrezia Tomes, David L Jones, Mary M Ogara, Earl J Seaver, Bonnie E Smith, Jayne M WachtelAbstract:Abstract Objective: To determine whether speech Hypernasality in subjects born with cleft palate can be reduced by graded velopharyngeal resistance training against continuous positive airway pressure (CPAP). Design: Pretreatment versus immediate posttreatment comparison study. Setting: Eight university and hospital speech clinics. Patients: Forty-three subjects born with cleft palate, aged 3 years 10 months to 23 years 8 months, diagnosed with speech Hypernasality. Intervention: Eight weeks of 6 days per week in-home speech exercise sessions, increasing from 10 to 24 minutes, speaking against transnasal CPAP increasing from 4 to 8.5 cm H20. Main Outcome Measure: Pretreatment to immediate posttherapy change in perceptual nasality score based on blinded comparisons of subjects' speech samples to standard reference samples by six expert clinician-investigators. Results: Participating clinical centers treated from two to nine eligible subjects, and results differed significantly across centers (interaction p...
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new therapy for treating hypernasal speech using continuous positive airway pressure cpap
Plastic and Reconstructive Surgery, 1991Co-Authors: David P KuehnAbstract:The purpose of this report is to introduce a new therapy technique for treating Hypernasality. The instrumentation consists of a continuous positive airway pressure (CPAP) device that delivers air pressure by means of a hose and nasal mask assembly to the nasal cavities. This positive pressure is theoretically useful in providing resistance training to strengthen the velopharyngeal closure muscles. Speech drillwork is conducted in the patient's home and consists of production of VNCV syllables and short sentences with the nasal mask worn by the patient. Incremental changes in CPAP pressure and time per therapy session occur over an 8-week course of therapy. Six case studies are presented. The preliminary results suggest that CPAP therapy may be effective in reducing Hypernasality in individuals exhibiting mild to moderate degrees of severity.
Yu-jeong Shin - One of the best experts on this subject based on the ideXlab platform.
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investigation of postoperative Hypernasality after superiorly based posterior pharyngeal flap
Maxillofacial plastic and reconstructive surgery, 2018Co-Authors: Yu-jeong Shin, Yongsoo KimAbstract:Velopharyngeal insufficiency that accompanies speech resonance and articulation disorders can be managed through several intervention methods such as speech-language therapy, prosthetic aids, and surgery. However, for patients with severe Hypernasality, surgical interventions are highly recommended. Among available surgical techniques, the posterior pharyngeal flap is most common. Two adult males with high nasalance scores underwent superiorly based posterior pharyngeal flap surgery, followed by speech testing by an expert speech-language therapist. Nasalance scores and articulation accuracy were assessed up until 1 year after the surgery. Nasalance scores were measured five times using a nasometer, after which the average value was calculated. Consistent declines in Hypernasality over time are not easy to explain since the pedicled pharyngeal flap narrowed over time, secondary to cicatrization. However, scar tethering of the soft palate in a posterior direction could reduce the velopharyngeal port size over time. Therefore, long-term follow-up with intensive speech therapy is suggested for patients with severe Hypernasality.
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Investigation of postoperative Hypernasality after superiorly based posterior pharyngeal flap
SpringerOpen, 2018Co-Authors: Yu-jeong Shin, Yongsoo KimAbstract:Abstract Background Velopharyngeal insufficiency that accompanies speech resonance and articulation disorders can be managed through several intervention methods such as speech-language therapy, prosthetic aids, and surgery. However, for patients with severe Hypernasality, surgical interventions are highly recommended. Among available surgical techniques, the posterior pharyngeal flap is most common. Case presentation Two adult males with high nasalance scores underwent superiorly based posterior pharyngeal flap surgery, followed by speech testing by an expert speech-language therapist. Nasalance scores and articulation accuracy were assessed up until 1 year after the surgery. Nasalance scores were measured five times using a nasometer, after which the average value was calculated. Conclusions Consistent declines in Hypernasality over time are not easy to explain since the pedicled pharyngeal flap narrowed over time, secondary to cicatrization. However, scar tethering of the soft palate in a posterior direction could reduce the velopharyngeal port size over time. Therefore, long-term follow-up with intensive speech therapy is suggested for patients with severe Hypernasality
Bruce E Murdoch - One of the best experts on this subject based on the ideXlab platform.
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an evaluation of continuous positive airway pressure cpap therapy in the treatment of Hypernasality following traumatic brain injury a report of 3 cases
Journal of Head Trauma Rehabilitation, 2004Co-Authors: Louise M Cahill, Deborah Theodoros, Aimee B Turner, Penelope A Stabler, Paula E Addis, Bruce E MurdochAbstract:Objective: To evaluate the effectiveness of continuous positive airway pressure (CPAP) therapy in the treatment of Hypernasality following traumatic brain injury (17111). Design: An A-B-A experimental research design. Assessments were conducted prior to commencement of the program, midway, immediately posttreatment, and 1 month after completion of the CPAP therapy program. Participants: Three adults with dysarthria and moderate to severe Hypernasality subsequent to TBI. Outcome Measures: Perceptual evaluation using the Frenchay Dysarthria Assessment, the Assessment of Intelligibility of Dysarthric Speech, and a speech sample analysis, and instrumental evaluation using the Nasometer. Results: Between assessment periods, varying degrees of improvement in Hypernasality and sentence intelligibility were noted. At the 1-month post-CPAP assessment, all 3 participants demonstrated reduced nasalance values, and 2 exhibited increased sentence intelligibility. Conclusions: CPAP may be a valuable treatment of impaired velopharyngeal function in the TBI population.
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Hypernasality in dysarthric speakers following severe closed head injury a perceptual and instrumental analysis
Brain Injury, 1993Co-Authors: Deborah Theodoros, Bruce E Murdoch, P D Stokes, Helen J CheneryAbstract:Hypernasality in the dysarthric speech of 20 severely closed-head- injured (CHI) subjects was investigated using both perceptual and instrumental techniques. A perceptual analysis of the speech of the CHI subjects was performed using a four-point rating scale for Hypernasality. Instrumental assessment was carried out using a computerized accelerometric technique yielding a nasal coupling index. Results revealed a high incidence of perceived Hypernasality (95%) in the speech of subjects in the CHI group. More than half of these subjects exhibited Hypernasality of speech to a moderate to severe degree. When compared with a control group matched for age and sex the severely CHI subjects were perceived as being significantly more hypernasal. Instrumental assessment revealed that the functioning of the velopharyngeal valve in the group of CHI subjects was significantly impaired compared to the control group. The study highlighted the need to evaluate the perceptual and instrumental assessment results for the s...
Heng Yin - One of the best experts on this subject based on the ideXlab platform.
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automatic Hypernasality grade assessment in cleft palate speech based on the spectral envelope method
Biomedizinische Technik, 2020Co-Authors: Jing Zhang, Xiyue Wang, Sen Yang, Ming Tang, Heng YinAbstract:Due to velopharyngeal incompetence, airflow overflows from the oral cavity to the nasal cavity, which results in Hypernasality. Hypernasality greatly reduces speech intelligibility and affects the daily communication of patients with cleft palate. Accurate assessment of Hypernasality grades can provide assisted diagnosis for speech-language pathologists (SLPs) in clinical settings. Utilizing a support vector machine (SVM), this paper classifies speech recordings into four grades (normal, mild, moderate and severe Hypernasality) based on vocal tract characteristics. Linear prediction (LP) analysis is widely used to model the vocal tract. Glottal source information may be included in the LP-based spectrum. The stabilized weighted linear prediction (SWLP) method, which imposes the temporal weights on the closed-phase interval of the glottal cycle, is a more robust approach for modeling the vocal tract. The extended weighted linear prediction (XLP) method weights each lagged speech signal separately, which achieves a finer time scale on the spectral envelope than the SWLP method. Tested speech recordings were collected from 60 subjects with cleft palate and 20 control subjects, and included a total of 4640 Mandarin syllables. The experimental results showed that the spectral envelope of normal speech decreases faster than that of hypernasal speech in the high-frequency part. The experimental results also indicate that the SWLP- and XLP-based methods have smaller correlation coefficients between normal and hypernasal speech than the LP method. Thus, the SWLP and XLP methods have better ability to distinguish hypernasal from normal speech than the LP method. The classification accuracies of the four Hypernasality grades using the SWLP and XLP methods range from 83.86% to 97.47%. The selection of the model order and the size of the weight function are also discussed in this paper.
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Hypernasalitynet deep recurrent neural network for automatic Hypernasality detection
International Journal of Medical Informatics, 2019Co-Authors: Xiyue Wang, Heng Yin, Sen Yang, Ming Tang, Hua HuangAbstract:Abstract Background Cleft palate patients have inability to produce adequate velopharyngeal closure, which results in hypernasal speech. In clinic, hypernasal speech is assessed through subject assessment by speech language pathologists. Automatic hypernasal speech detection can provide aided diagnoses for speech language pathologists and clinicians. Objectives This study aims to develop Long Short-Term Memory (LSTM) based Deep Recurrent Neural Network (DRNN) system to detect hypernasal speech from cleft palate patients, thus to provide aided diagnoses for clinical operation and speech therapy. Meanwhile, the feature mining and classification abilities of LSTM-DRNN system are explored. Methods The utilized speech recordings are 14,544 vowels in Mandarin. Speech data is collected from 144 children (72 children with Hypernasality and 72 controls) with the age of 5–12 years old. This work proposes a LSTM based DRNN system to achieve automatic hypernasal speech detection, since LSTM-DRNN can learn short-time dependences of hypernasal speech. The vocal tract based features are fed into LSTM-DRNN to achieve deep mining of features. To verify the feature mining ability of LSTM-DRNN, features projected by LSTM-DRNN are fed into shallow classifiers instead of the following two fully connected layers and a softmax layer. And the features without the projecting process of LSTM-DRNN are directly fed into shallow classifiers as a comparison. Hypernasality-sensitive vowels (/a/, /i/, and /u/) are analyzed for the first time. Results This LSTM-DRNN based hypernasal speech detection method reaches higher detection accuracy than that using shallow classifiers, since LSTM-DRNN mines features through time axis and network depth simultaneously. The proposed LSTM-DRNN based Hypernasality detection system reaches the highest accuracy of 93.35%. According to the analysis of Hypernasality-sensitive vowels, the experimental result concludes that vowels /i/ and /u/ are the most sensitive vowels to hypernasal speech. Conclusions The results show that LSTM-DRNN has robust feature mining ability and classification ability. This is the first work that applies the LSTM-DRNN technique to automatically detect Hypernasality in cleft palate speech. The experimental results demonstrate the potential of deep learning on pathologist speech detection.
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Automatic Hypernasality Detection in Cleft Palate Speech Using CNN
Circuits Systems and Signal Processing, 2019Co-Authors: Xiyue Wang, Heng Yin, Sen Yang, Hua Huang, Ming Tang, Ling HeAbstract:Automatic Hypernasality detection in cleft palate speech can facilitate diagnosis by speech-language pathologists. This paper describes a feature-independent end-to-end algorithm that uses a convolutional neural network (CNN) to detect Hypernasality in cleft palate speech. A speech spectrogram is adopted as the input. The average F1-scores for the Hypernasality detection task are 0.9485 and 0.9746 using a dataset that is spoken by children and a dataset that is spoken by adults, respectively. The experiments explore the influence of the spectral resolution on the Hypernasality detection performance in cleft palate speech. Higher spectral resolution can highlight the vocal tract parameters of Hypernasality, such as formants and spectral zeros. The CNN learns efficient features via a two-dimensional filtering operation, while the feature extraction performance of shallow classifiers is limited. Compared with deep neural network and shallow classifiers, CNN realizes the highest F1-score of 0.9485. Comparing various network architectures, the convolutional filter of size 1 × 8 achieves the highest F1-score in the Hypernasality detection task. The selected filter size of 1 × 8 considers more frequency information and is more suitable for Hypernasality detection than the filters of size 3 × 3, 4 × 4, 5 × 5, and 6 × 6. According to an analysis of Hypernasality-sensitive vowels, the experimental result concludes that the vowel /i/ is the most sensitive vowel to Hypernasality. Compared with state-of-the-art literature, the proposed CNN-based system realizes a better detection performance. The results of an experiment that is conducted on a heterogeneous corpus demonstrate that CNN can better handle the speech variability compared with the shallow classifiers.
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automatic evaluation of Hypernasality based on a cleft palate speech database
Journal of Medical Systems, 2015Co-Authors: Jing Zhang, Heng Yin, Qi Liu, Margaret Lech, Yunzhi HuangAbstract:The Hypernasality is one of the most typical characteristics of cleft palate (CP) speech. The evaluation outcome of Hypernasality grading decides the necessity of follow-up surgery. Currently, the evaluation of CP speech is carried out by experienced speech therapists. However, the result strongly depends on their clinical experience and subjective judgment. This work aims to propose an automatic evaluation system for Hypernasality grading in CP speech. The database tested in this work is collected by the Hospital of Stomatology, Sichuan University, which has the largest number of CP patients in China. Based on the production process of Hypernasality, source sound pulse and vocal tract filter features are presented. These features include pitch, the first and second energy amplified frequency bands, cepstrum based features, MFCC, short-time energy in the sub-bands features. These features combined with KNN classier are applied to automatically classify four grades of Hypernasality: normal, mild, moderate and severe. The experiment results show that the proposed system achieves a good performance. The classification rates for four Hypernasality grades reach up to 80.4 %. The sensitivity of proposed features to the gender is also discussed.
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automatic evaluation of Hypernasality and consonant misarticulation in cleft palate speech
IEEE Signal Processing Letters, 2014Co-Authors: Jing Zhang, Heng Yin, Qi Liu, Margaret LechAbstract:The automatic evaluation of CP (Cleft Plate) speech has various clinical applications. In this work, automatic classification of Hypernasality levels and detection of consonant omission methods are proposed. Considering that the data collection is a major bottleneck in the field of CP speech signal processing, an extensive CP speech database is used. This database is collected over 10 years by the Hospital of Stomatology, Sichuan University, which has the largest number of CLP (Cleft Lip and Palate) patients in China. The vocabulary used for this database covers all the initial consonants and the most widely used vowels in Mandarin. Based on the production process of Hypernasality, a two-step classification algorithm is proposed to identify four levels of Hypernasality: normal, mild, moderate and severe. In order to locate consonant omission, an effective classification algorithm is proposed through analyzing the acoustic characteristics of initial consonants and finals. The classification accuracy for four Hypernasality levels reaches up to 83% and the correct identification of consonant omission is over 94%.