Estimation of Severity of Speech Disability Through Speech Envelope

Gudi, Anandthirtha B.; Shreedhar, H. K.; Nagaraj, H. C.

doi:10.5121/sipij.2011.2203

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main focus of the work presented in [105] is to detect voice disability among children. In that work, the authors use the envelope of voice signals to detect pathological cases of speech-disabled children.…”

Section: Time Domainmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Signal Processing Based Pathological Voice Detection Techniques

2020

View full text Add to dashboard Cite

Voice disability is a barrier to effective communication. Around 1.2% of the World's population is facing some form of voice disability. Surgical procedures namely laryngoscopy, laryngeal electromyography, and stroboscopy are used for voice disability diagnosis. Researchers and practitioners have been working to find alternatives to these surgical procedures. Voice sample based diagnosis is one of them. The major steps followed by these works are (a) to extract voice features from voice samples and (b) to discriminate pathological voices from normal voices by using a classifier algorithm. However, there is no consensus about the voice feature and the classifier algorithm that can provide the best accuracy in screening voice disability. Moreover, some of the works use multiple voice features and multiple classifiers to ensure high reliability. In this paper, we address these issues. The motivation of the work is to address the need for non-invasive signal processing techniques to detect voice disability in the general population. This paper conducts a survey related to voice disability detection methods. The paper contains two main parts. In the first part, we present background information including causes of voice disability, current procedures and practices, voice features, and classifiers. In the second part, we present a comprehensive survey work on voice disability detection algorithms. The issues and challenges related to the selection of voice feature and classifier algorithms have been addressed at the end of this paper. INDEX TERMS Algorithms, issues and challenges, signal processing, surgical methods, survey, voice disability, voice features.

show abstract

Section: Time Domainmentioning

confidence: 99%

“…It is mostly recommended that frequency domain voice features are more helpful for detecting voice disability. However, some researchers also argue that timedomain features are more helpful for detecting voice disability [105]- [108].…”

Section: E Voice Featuresmentioning

confidence: 99%