Speech Signal Processing Based on Wavelets and SVM for Vocal Tract Pathology Detection

Kukharchik, P. D.; Kheidorov, Igor E.; Bovbel, E.I.; Ladeev, D.

doi:10.1007/978-3-540-69905-7_22

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…However, these techniques have some cons as human vocal tract is hardly accessible during phonation process hindering proper identification of pathology. Additionally, these diagnostic means are not comfortable and can distort the signal leading to incorrect diagnosis [150]. Consequently, the area of non-invasive assessment of health state of a person is rising due to its robust nature, low-cost, comfort and absence of subjective biasness.…”

Section: A Personal Attributes Inferencementioning

confidence: 99%

A Comprehensive Study of Audio Profiling: Methods, Applications, Challenges, and Future Directions

Pudasaini,

Al-Hawawreh,

Bouadjenek

et al. 2024

Preprint

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Audio profiling is at the forefront of a technological breakthrough, offering rich insights into human behavior, emotions, physical attributes, and environmental contexts through detailed analysis of voice data. As we embrace an era where the integration of smart technologies equipped with the ability to capture sound is becoming omnipresent, the capacity to accurately infer personal traits such as age, gender, height, weight, emotional state, personality, and even environmental contexts through voice analysis opens up vast opportunities across law enforcement, healthcare, social and commercial services, and entertainment. This emerging field promises to enhance our interaction with technology by not only understanding who we are but also by interpreting the world around us. However, the exciting landscape is fraught with challenges, including data imbalances, the complexity of predictive models, and significant privacy concerns regarding handling sensitive paralinguistic information. This survey explores deep into the current landscape of audio profiling, examining the techniques and datasets in use, and showcasing its diverse applications while highlighting the need for advanced methodologies, enriched dataset development, and robust privacy preservation techniques.

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Section: A Personal Attributes Inferencementioning

confidence: 99%

A Comprehensive Study of Audio Profiling: Methods, Applications, Challenges, and Future Directions

Pudasaini,

Al-Hawawreh,

Bouadjenek

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Alternatively, features stemming from the 1-D bicoherence index derived by the bispectrum [22] or nonlinear dynamical system theory, such as statistics of the correlation dimension and the largest Lyapunov exponent [26], or the return period density entropy [27] were extracted. Features could also be obtained by applying the continuous wavelet transform to each speech frame and averaging neighbor wavelet coefficients on timefrequency scale [28]. Frequently, feature vectors undergo dimensionality reduction by applying Principal Component Analysis (PCA) [29][30][31] before classification or a subset of features are selected by applying either a wrapper or a filter.…”

Section: Introductionmentioning

confidence: 99%

“…That is, to verify a specific pathology in a test utterance or to decide whether a test utterance is pathological or not. Commonly used classifiers resort to linear discriminant analysis (LDA) [23,27,29,32], nearest neighbors [24,26,29], vector quantization [33] or support vector machines (SVMs) [28,31,34]. It is worth noting that the detection of voice pathology is closely related to speaker verification.…”

Section: Introductionmentioning

confidence: 99%

Linear Classifier with Reject Option for the Detection of Vocal Fold Paralysis and Vocal Fold Edema

Kotropoulos

Arce

2009

EURASIP J. Adv. Signal Process.

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Two distinct two-class pattern recognition problems are studied, namely, the detection of male subjects who are diagnosed with vocal fold paralysis against male subjects who are diagnosed as normal and the detection of female subjects who are suffering from vocal fold edema against female subjects who do not suffer from any voice pathology. To do so, utterances of the sustained vowel "ah" are employed from the Massachusetts Eye and Ear Infirmary database of disordered speech. Linear prediction coefficients extracted from the aforementioned utterances are used as features. The receiver operating characteristic curve of the linear classifier, that stems from the Bayes classifier when Gaussian class conditional probability density functions with equal covariance matrices are assumed, is derived. The optimal operating point of the linear classifier is specified with and without reject option. First results using utterances of the "rainbow passage" are also reported for completeness. The reject option is shown to yield statistically significant improvements in the accuracy of detecting the voice pathologies under study.

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Model optimization of SVM for a fermentation soft sensor

Liu

Zhou

et al. 2010

Expert Systems with Applications

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Speech Signal Processing Based on Wavelets and SVM for Vocal Tract Pathology Detection

Cited by 7 publications

References 6 publications

A Comprehensive Study of Audio Profiling: Methods, Applications, Challenges, and Future Directions

A Comprehensive Study of Audio Profiling: Methods, Applications, Challenges, and Future Directions

Linear Classifier with Reject Option for the Detection of Vocal Fold Paralysis and Vocal Fold Edema

Model optimization of SVM for a fermentation soft sensor

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