• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.16 no.2
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• pp.140-145
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• 2005

Background and Objectives : Voice perturbation measures, such as jitter and shimmer has been importantly used for diagnosis and treatment efficacy of laryngeal dysfunction. This study was conducted to investigate validity of newly developed multi-channel voice analyzer program by comparing with MDVP, PRAAT, TF32. In addition, we compared the voice perturbation measures with different voice analyzer program by type of signals. Materials and Methods : Nineteen mild-severe dysphonic patients participated in our study. Fundamental frequency, jitter and shimmer values were obtained from different voice analyzer program using the same sustained/ah/phonation. Results : Fundamental frequency and shimmer were highly correlated whereas jitter was weakly correlated between newly developed multi-channel voice analyzer program and the others though different pitch computation algorithm except MDVP, In addition, Type 2 and 3 signals were weakly correlated than Type 1. Conclusion : In the clinical setting, clinician may have sufficient information of voice analyzer and control conditions properly for severity of pathologic voice before voice perturbation measure to obtain reliable results.

• Phonetics and Speech Sciences
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• v.1 no.4
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• pp.147-157
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• 2009

The purpose of the study is to develop the normal voice database and to analyze the acoustic characteristics of Korean adults' voices by gender using MDVP. Eight categories in the 34 parameters of MDVP were analyzed in the voices of 170 Korean normal adults taken from /a/ vowel. Among them, Fundamental Frequency Parameters and Frequency Perturbation Parameters were significantly different by gender. In addition, Fundamental Frequency Parameters of our data were remarkably different from the data suggested in the MDVP program which currently used in clinics. Therefore, the data obtained from the current study can be effectively used for the diagnosis of voice disorders of Korean adults as the standard parameter values of MDVP.

• Speech Sciences
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• v.15 no.2
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• pp.89-97
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• 2008

There is a number of clinical conditions that affect directly or indirectly the physical properties of the vocal folds and thereby the pressure waveforms of elicited sounds. If the relationships between the clinical conditions and the voice quality are sufficiently reliable, it should be possible to detect these diseases or disorders. The focus of this paper is to determine the set of features and their values that would characterize the speaker's state of vocal folds. To the extent that these features can capture the anatomical, physiological, and neurological aspects of the speaker they can be potentially used to mediate an unobtrusive approach to diagnosis. We will show a new approach to this problem supported with results obtained from two disordered voice corpora.

• Speech Sciences
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• v.3
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• pp.5-17
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• 1998

Botulinum toxin, a neurotoxin derived from Clostridia Botulinum, has been injected into the target muscle(s) for the treatment of several kinds of voice and speech disorders at the Voice Clinic, Yonsei Institute of Logopedics and Phoniatrics since December 1995. Criteria for the diagnosis and method of injection for spasmodic dysphonia, mutational dysphonia, muscle tension dysphonia, dysphonia after total laryngectomy, and stuttering were summarized. Among 144 patients with adductor type spasmodic dysphonia, who were injected one time to maximum 8 times during the 27 months, 90% were recognized as having better than slight improvement. Even though the injected cases were small, not only the abductor type spasmodic dysphonia, but also the intractable mutational dysphonia or muscle tension dysphonia resistant to voice therapy revealed that botulinum toxin injection would be another options for treatment. Patients who cannot phonate after total laryngectomy and some forms of adulthood stutterers can also be candidates for the injection of botulinum toxin.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.431-437
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• 2013

These days, many people live a healthy life, but suffering caused by chronic diseases. The main factors of chronic diseases are stress, blood pressure and obesity. Chronic diseases which are caused by high blood pressure are very high incidence. Therefore, this paper suggests the ways to prevent as diagnosis a phenomenon that occur rising in blood pressure consistently by analyzing the voice according th rising in blood pressure. For this, I studied some influence on voicing through rising in blood pressure by applying pitch that measure vocal fold vibration and intensity that measure voice energy size that is one of technique. That collect and analyse the voice after rising blood pressure by aerobic exercise.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.3
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• pp.142-155
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• 2022

Diagnosis using voice is non-invasive and can be implemented through various voice recording devices; therefore, it can be used as a screening or diagnostic assistant tool for laryngeal voice disease to help clinicians. The development of artificial intelligence algorithms, such as machine learning, led by the latest deep learning technology, began with a binary classification that distinguishes normal and pathological voices; consequently, it has contributed in improving the accuracy of multi-classification to classify various types of pathological voices. However, no conclusions that can be applied in the clinical field have yet been achieved. Most studies on pathological speech classification using speech have used the continuous short vowel /ah/, which is relatively easier than using continuous or running speech. However, continuous speech has the potential to derive more accurate results as additional information can be obtained from the change in the voice signal over time. In this review, explanations of terms related to artificial intelligence research, and the latest trends in machine learning and deep learning algorithms are reviewed; furthermore, the latest research results and limitations are introduced to provide future directions for researchers.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.21 no.1
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• pp.17-21
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• 2010

Dysphonia is a medical terminology for voice disorders characterized by hoarseness, harshness, weakness, or even loss of voice ; any impairment in ability to produce voice sounds using the vocal organs, larynx, The causes of dysphonia can be classified into two groups, organic and functional. Functional dysphonia includes spasmodic dysphonia, muscle tension dysphonia, mutational dysphonia and conversion dysphonia, etc, The findings of laryngoscopy in these dysphonia are almost normal. Therefore, physicians should diagnosis these diseases from careful history taking and abundant understandings about the phonation pattern, Organic dysphonia is caused by anatomical problems in the larynx, especially on the vocal fold, Some lesions, however, are not easily found because these lesions are too small, or located on the lower lip of vibrating vocal fold. Laryngopharyngeal reflux induced laryngitis, vascular lesions, sulcus vocalis, vocal atropy including presbylaryngis, and mucosal tears are common lesions easily missed in laryngoscopy, Therefore, a high index of suspicion is necessary to avoid missing vocal fold mucosal lesions, and the strobovideolaryngoscopy is indispensable in making the diagnosis,

• Journal of Digital Convergence
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• v.19 no.12
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• pp.353-358
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• 2021

Parkinson's disease is the second most common degenerative brain disease after Alzheimer's in old age. Symptoms of Parkinson's disease are factors that reduce the quality of life in daily life, such as shaking hands, slowing behavior and cognitive function. Parkinson's disease that can slow the progression of the disease through early diagnosis. To diagnoze Parkinson's disease early, an algorithm was implemented to extract features using wav2vec and to diagnose the presence or absence of Parkinson's disease with deep learning(ANN). As a results of the experiment, the accuracy was 97.47%. It was better than the results of diagnosing Parkinson's disease using the existing neural network. The audio voice file could simply reduce the experiment process and obtain improved results.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.32 no.3
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• pp.124-129
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• 2021

Background and Objectives This study compares Vocal Fatigue Index (VFI) scores according to the presence or absence of external laryngeal tension in hyperfunctional voice disorder. And through this, it is to confirm the usefulness of VFI to hypertension of extrinsic laryngeal muscles. Materials and Method The subjects were 61 female diagnosed with hyperfunctional voice disorder (hypertension group 41, non-hypertension group 20). The author palpated extrinsic laryngeal muscles for evaluation of hypertension and classified them as the presence or absence. The voice measurements were jitter, shimmer, Korean-Voice Handicap Index-10 (K-VHI-10), and Korean-Vocal Fatigue Index (K-VFI). The voice compared were according to the diagnosis and presence of hypertension only for patients with hyperfunctional voice disorder. Results As a result of comparing the voice measurement according to the presence or absence of hypertension, there was no significant difference in the acoustic variables, K-VHI-10 and K-VFI-Total, K-VFI-Fatigue. Whereas, K-VFI-Physical (p=0.006) and K-VFI-Rest (p=0.022) were significantly higher in the hypertension group. Conclusion These results indicate that the hypertension group has more physical discomfort and less voice recovery than the group without hypertension. It means that K-VFI can measure the physical discomfort and limitations of voice recovery due to hypertension of the external laryngeal muscle. The VFI can be used as one of the methods to evaluate the hypertension of the external laryngeal muscle in Hyperfunctional voice disorder.

• Journal of Sasang Constitutional Medicine
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• v.22 no.1
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• pp.17-25
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• 2010

1. Objectives: Voice diagnosis has been used to classify individuals into the Sasang constitution in SCM(Sasang Constitution Medicine) and to recognize his/her health condition in TKM(Traditional Korean Medicine). In this paper, we purposed a new speech classification algorithm for Sasang constitution. 2. Methods: This algorithm is based on the SVM(Support Vector Machine) technique, which is a classification method to classify two distinct groups by finding voluntary nonlinear boundary in vector space. It showed high performance in classification with a few numbers of trained data set. We designed for this algorithm using 3 SVM classifiers to classify into 4 groups, which are composed of 3 constitutional groups and additional indecision group. 3. Results: For the optimal performance, we found that 32.2% of the voice data were classified into three constitutional groups and 79.8% out of them were grouped correctly. 4. Conclusions: This new classification method including indecision group appears efficient compared to the standard classification algorithm which classifies only into 3 constitutional groups. We find that more thorough investigation on the voice features is required to improve the classification efficiency into Sasang constitution.