The usefulness of multi voice evaluation: Development of a model for predicting a degree of dysphonia

Lee, YeonWoo; Park, HeeJune; Bae, In-Ho; Kim, Geun‐Hyo

doi:10.1016/j.jvoice.2020.10.020

Cited by 9 publications

(3 citation statements)

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“…Due to its repeatable features, this procedure provides the opportunity to conduct comparisons [11]. At present, the parameters that are most extensively utilized by physicians and cited in research are Fundamental Frequency (F0), which shows the cycle of the wave; Jitter, which refers to the degree of frequency variability in a sound wave and is typically stated as a word that distinguishes the cycle of F0; Shimmer, which is a quantification of the degree of variation in amplitude inside a sound wave; and Harmonics-to-Noise-Ratio (HNR), which quantifies the amount of additional interference present in human vocal signals caused by a leakage in the closure of the vocal folds during speech production [14][15][16][17]. Vowel metric analysis is widely used in clinical research to associate dysarthria conditions with pathological vocal signals [11,18].…”

Section: Methodsmentioning

confidence: 99%

Using Objective Speech Analysis Techniques for the Clinical Diagnosis and Assessment of Speech Disorders in Patients with Multiple Sclerosis

Sonkaya,

Özturk,

Sonkaya

et al. 2024

Brain Sciences

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Multiple sclerosis (MS) is one of the chronic and neurodegenerative diseases of the central nervous system (CNS). It generally affects motor, sensory, cerebellar, cognitive, and language functions. It is thought that identifying MS speech disorders using quantitative methods will make a significant contribution to physicians in the diagnosis and follow-up of MS patients. In this study, it was aimed to investigate the speech disorders of MS via objective speech analysis techniques. The study was conducted on 20 patients diagnosed with MS according to McDonald’s 2017 criteria and 20 healthy volunteers without any speech or voice pathology. Speech data obtained from patients and healthy individuals were analyzed with the PRAAT speech analysis program, and classification algorithms were tested to determine the most effective classifier in separating specific speech features of MS disease. As a result of the study, the K-nearest neighbor algorithm (K-NN) was found to be the most successful classifier (95%) in distinguishing pathological sounds which were seen in MS patients from those in healthy individuals. The findings obtained in our study can be considered as preliminary data to determine the voice characteristics of MS patients.

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Section: Methodsmentioning

confidence: 99%

Using Objective Speech Analysis Techniques for the Clinical Diagnosis and Assessment of Speech Disorders in Patients with Multiple Sclerosis

Sonkaya,

Özturk,

Sonkaya

et al. 2024

Brain Sciences

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“…Speech contains substantial information that can be used as acoustic biomarkers to monitor patient status, diagnose conditions, classify diseases, or develop relevant drugs ( Abrahamsson et al, 2018 ; Kraus, 2018 ; Noffs et al, 2018 ; Lee et al, 2020 ; Fagherazzi et al, 2021 ). Objective speech assessment is more accurate, replicable, and feasible than perceptual analysis ( Noffs et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

A rapid, non-invasive method for fatigue detection based on voice information

Gao¹,

Ma²,

Yang³

et al. 2022

Front. Cell Dev. Biol.

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Fatigue results from a series of physiological and psychological changes due to continuous energy consumption. It can affect the physiological states of operators, thereby reducing their labor capacity. Fatigue can also reduce efficiency and, in serious cases, cause severe accidents. In addition, it can trigger pathological-related changes. By establishing appropriate methods to closely monitor the fatigue status of personnel and relieve the fatigue on time, operation-related injuries can be reduced. Existing fatigue detection methods mostly include subjective methods, such as fatigue scales, or those involving the use of professional instruments, which are more demanding for operators and cannot detect fatigue levels in real time. Speech contains information that can be used as acoustic biomarkers to monitor physiological and psychological statuses. In this study, we constructed a fatigue model based on the method of sleep deprivation by collecting various physiological indexes, such as P300 and glucocorticoid level in saliva, as well as fatigue questionnaires filled by 15 participants under different fatigue procedures and graded the fatigue levels accordingly. We then extracted the speech features at different instances and constructed a model to match the speech features and the degree of fatigue using a machine learning algorithm. Thus, we established a method to rapidly judge the degree of fatigue based on speech. The accuracy of the judgment based on unitary voice could reach 94%, whereas that based on long speech could reach 81%. Our fatigue detection method based on acoustic information can easily and rapidly determine the fatigue levels of the participants. This method can operate in real time and is non-invasive and efficient. Moreover, it can be combined with the advantages of information technology and big data to expand its applicability.

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“…As an example, the "Italian Society of Phoniatrics and Speech Therapy", or Società Italiana di Foniatria e Logopedia (SIFEL) [45], classifies the quality of an individual's voice based on the analysis of characteristics associated with the signal, namely the fundamental frequency [46], the jitter and shimmer variations [47], and the harmonic-to-noise ratio (HNR) [48]. However, the manual classification of voice quality through the analysis of these parameters is very dependent on the algorithm performance that estimates these metrics and the clinical professional responsible for examining them [49,50], which makes the scalability of this procedure difficult, making it subjective, and consequently, reducing its accuracy. Thus, in view of the increased accuracy in detecting the presence of a disorder in a patient's voice through the investigation of parameters associated with the sound signal, techniques based on the extraction of signal features and the classification of these features with machine learning methods have become popular on the topic in recent years, as demonstrated in the review works of Al-Hussain et al [29] and Hegde et al [30].…”

Section: Related Workmentioning

confidence: 99%

An Experimental Analysis on Multicepstral Projection Representation Strategies for Dysphonia Detection

Contreras

Viana

Fonseca

et al. 2023

Sensors

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Biometrics-based authentication has become the most well-established form of user recognition in systems that demand a certain level of security. For example, the most commonplace social activities stand out, such as access to the work environment or to one’s own bank account. Among all biometrics, voice receives special attention due to factors such as ease of collection, the low cost of reading devices, and the high quantity of literature and software packages available for use. However, these biometrics may have the ability to represent the individual impaired by the phenomenon known as dysphonia, which consists of a change in the sound signal due to some disease that acts on the vocal apparatus. As a consequence, for example, a user with the flu may not be properly authenticated by the recognition system. Therefore, it is important that automatic voice dysphonia detection techniques be developed. In this work, we propose a new framework based on the representation of the voice signal by the multiple projection of cepstral coefficients to promote the detection of dysphonic alterations in the voice through machine learning techniques. Most of the best-known cepstral coefficient extraction techniques in the literature are mapped and analyzed separately and together with measures related to the fundamental frequency of the voice signal, and its representation capacity is evaluated on three classifiers. Finally, the experiments on a subset of the Saarbruecken Voice Database prove the effectiveness of the proposed material in detecting the presence of dysphonia in the voice.

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The usefulness of multi voice evaluation: Development of a model for predicting a degree of dysphonia

Cited by 9 publications

References 50 publications

Using Objective Speech Analysis Techniques for the Clinical Diagnosis and Assessment of Speech Disorders in Patients with Multiple Sclerosis

Using Objective Speech Analysis Techniques for the Clinical Diagnosis and Assessment of Speech Disorders in Patients with Multiple Sclerosis

A rapid, non-invasive method for fatigue detection based on voice information

An Experimental Analysis on Multicepstral Projection Representation Strategies for Dysphonia Detection

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