Vocal Acoustic Analysis – Jitter, Shimmer and HNR Parameters

Teixeira, João Paulo; Oliveira, Carla; Lopes, Carla

doi:10.1016/j.protcy.2013.12.124

Cited by 304 publications

(143 citation statements)

References 4 publications

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“…As an example, figure 1 shows the main representation of jitter and shimmer features in voice signal (Teixeira et al, 2013).…”

Section: Features Extractionmentioning

confidence: 99%

Pathological Voice Signal Analysis Using Machine Learning Based Approaches

Alemami¹,

Almazaydeh²

2017

CIS

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Voice signal analysis is becoming one of the most significant examination in clinical practice due to the importance of extracting related parameters to reflect the patient's health. In this regard, various acoustic studies have been revealed that the analysis of laryngeal, respiratory and articulatory function may be efficient as an early indicator in the diagnosis of Parkinson disease (PD). PD is a common chronic neurodegenerative disorder, which affects a central nervous system and it is characterized by progressive loss of muscle control. Tremor, movement and speech disorders are the main symptoms of PD. The diagnosis decision of PD is obtained by continued clinical observation which relies on expert human observer. Therefore, an additional diagnosis method is desirable for most comfortable and timely detection of PD as well as faster treatment is needed. In this study, we develop and validate automated classification algorithms, which are based on Naïve Bayes and K-Nearest Neighbors (KNN) using voice signal measurements to predict PD. According to the results, the diagnostic performance provided by the automated classification algorithm using Naïve Bayes was superior to that of the KNN and it is useful as a predictive tool for PD screening with a high degree of accuracy, approximately 93.3%.

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“…As an example, figure 1 shows the main representation of jitter and shimmer features in voice signal (Teixeira et al, 2013).…”

Section: Features Extractionmentioning

confidence: 99%

Pathological Voice Signal Analysis Using Machine Learning Based Approaches

Alemami¹,

Almazaydeh²

2017

CIS

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“…It is responsible of the tonal and rhythmic properties of speech. It is defined as the number of times in which a sound wave produced by vocal cords is repeated during a specific time period [16]. The fundamental frequency represents the lowest tone of the harmonic series generated by the vocal fold vibration.…”

Section: Acoustic Analysismentioning

confidence: 99%

“…In the last few years, traditional and new acoustic measures of dysarthric speech have been proposed as alternative methods to differentiate dysarthric from healthy speech [14,15]. The authors in [16] analyzed acoustic parameters as well as fundamental frequency (F 0 ), jitter, shimmer and Harmonics-to-Noise-Ratio (HNR) measures to define a procedure for the automatic diagnosis of larynx pathologies. In [17], a set of vowel metrics are presented, as derived from spectral and temporal measurements of vowel tokens embedded in phrase production, used to distinguish healthy from dysarthric speech.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Voice Signals for Studying Sclerosis Disease

et al. 2017

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Abstract:Multiple sclerosis (MS) is a chronic demyelinating autoimmune disease affecting the central nervous system. One of its manifestations concerns impaired speech, also known as dysarthria. In many cases, a proper speech evaluation can play an important role in the diagnosis of MS. The identification of abnormal voice patterns can provide valid support for a physician in the diagnosing and monitoring of this neurological disease. In this paper, we present a method for vocal signal analysis in patients affected by MS. The goal is to identify the dysarthria in MS patients to perform an early diagnosis of the disease and to monitor its progress. The proposed method provides the acquisition and analysis of vocal signals, aiming to perform feature extraction and to identify relevant patterns useful to impaired speech associated with MS. This method integrates two well-known methodologies, acoustic analysis and vowel metric methodology, to better define pathological compared to healthy voices. As a result, this method provides patterns that could be useful indicators for physicians in identifying patients affected by MS. Moreover, the proposed procedure could be a valid support in early diagnosis as well as in monitoring treatment success, thus improving a patient's life quality.

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“…The proposed assessment also some questions adapted from Eckelman-Taylor (Speech and Hearing clinic, Illinois state university) [38] speech and language adult case history such as including: Have you received speech therapy services in the past? Have you ever experienced any of the following: difficulty saying some syllables or/and recalling words you wish to say?…”

Section: Assessment In Speech-language Pathologymentioning

confidence: 99%

Development of a Minimum Protocol for the Assessment of Communication Disorders within Moroccan Students

Sabir¹,

Touri²,

Mahtar³

2016

TOPHJ

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Background:The present paper aims at establishing a minimum assessment protocol to estimate the prevalence of communication disorders, their severity and the correlation between identified communication disorders and oral communication activities among students. Material and Methods:A questionnaire was built using assessments published in current literature, which consists of 14 sections.The first section gathers demographic information such as age, gender, and level of education as well as history of speech therapy services (e.g. "do you consult a speech therapist?").The second section asks about the ability to communicate verbally in public in social situations and the possible causes that impact a student's ability to communicate effectively.The third section deals with the attitudes of the student before, during and after communicating, specifically focusing on questions related to stuttering.The proposed assessment also identified the severity of stuttering as well as any secondary behaviours associated with stuttering (e.g. eye blinking, tics, face spasms, etc.).Sections 4 through 13 are concerned with questions related to communication disorders such as: articulation disorders, language delays, dysphasia, swallowing disorders, hearing loss, cleft lip or palate, aphasia, attention disorders, dyslexia, and dysphonia.The last section deals with acoustic analysis, using Praat Software, where a recording of the sustained vowel /a/ is performed by each participant in order to have an overview of the following acoustic parameters: pitch, harmonics to noise ratio, shimmer and jitter.Participants included in this study were between 18 and 22 years of age who were in their 1 st or 2 nd year of department of science of the Ben M'Sik College -University Hassan II Mohammedia. Results:The questionnaire was completed by 170 students, which insures a confidence interval of 4 and confidence level of 95%.Among the sample, 58% of students are male, and 42% are female.The studied sample demonstrated that 75% of the students do not feel comfortable when engaging in verbal communication in social situation, which was highly correlated with stuttering.Lack of self-confidence, stage fright, lack of experience, shyness and the presence of an important group are 80% of the causes students do not feel at ease when publicly speaking in front of an unfamiliar group. The study revealed that 20% of students had fluency disorders, 15% had articulation disorders, 21% had swallowing disorders, 19% had dysphasia, 1% of the students had hearing loss, 14% had attention disorders, 10% had dyslexia, and 6% had dysphonia.The major finding of the present paper was that 54% of the students studied were identified as having a language delay.Subjective assessments of the communication disorders are complemented with objective assessments based on acoustic analysis of the sustained vowel /a/ recorded from the studied sample.Based on the conducted research, we found that communication disorders impact the oral communication activities with ...

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Vocal Acoustic Analysis – Jitter, Shimmer and HNR Parameters

Cited by 304 publications

References 4 publications

Pathological Voice Signal Analysis Using Machine Learning Based Approaches

Pathological Voice Signal Analysis Using Machine Learning Based Approaches

On the Use of Voice Signals for Studying Sclerosis Disease

Development of a Minimum Protocol for the Assessment of Communication Disorders within Moroccan Students

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