Voice Parameter Analysis for the disease detection

Dixit, Dixit; Mittal, Vikas; Sharma, Yuvraj

doi:10.9790/2834-09314855

Cited by 13 publications

(5 citation statements)

References 43 publications

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“…1. Voice analysis expansion [19]: The tonal quality, pitch fluctuations, speech rate, and pause patterns in voice data can be quantitatively analyzed to detect early subtle signs of cognitive decline or emotional distress. For example, monotone speech may be an early 8.…”

Section: Future Developmentmentioning

confidence: 99%

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

Biró,

Cuesta-Vargas,

Szilágyi

2023

Acta Universitatis Sapientiae, Informatica

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The paper examines the potential of artificial intelligence (AI) in parsing text and conducting sentiment analysis to identify early markers of mental health and neurodegenerative disorders. Through the analysis of textual data, we investigate whether AI can provide a noninvasive, continuous, and objective complement to traditional diagnostic practices. Background: the early detection of mental health (such as depression, anxiety, psychotic disorders, Alzheimer’s disease and dementia) and neurodegenerative disorders (like Parkinson’s disease) remains a critical challenge in clinical practice. Traditional diagnostic methods rely on clinical evaluations that may be subjective and episodic. Recent advancements in AI and natural language processing (NLP) have opened new avenues for precognitive health assessments, suggesting that variations in language and expressed sentiments in written text can serve as potential biomarkers for these conditions. Materials and Methods: the research used a dataset comprising various forms of textual data, including anonymized social media interactions, transcripts from patient interviews, and electronic health records. NLP algorithms were deployed to parse the text, and machine learning models were trained to identify language patterns and sentiment changes. The study also incorporated a sentiment analysis to gauge emotional expression, a key component of mental health diagnostics. Results: the AI models were able to identify language use patterns and sentiment shifts that correlated with clinically validated instances of mental health symptoms and neurodegenerative conditions. Notably, the models detected an increased use of negative a ect words, a higher frequency of first-person singular pronouns, and a decrease in future tense in individuals with depression. For neurode-generative conditions, there was a notable decline in language complexity and semantic coherence over time. Conclusions: the implemented pipeline of AI-parsed text and sentiment analysis appears to be a promising tool for the early detection and ongoing monitoring of mental health and neurodegenerative disorders. However, these methods are supplementary and cannot replace the nuanced clinical evaluation process. Future research must refine the AI algorithms to account for linguistic diversity and context, while also addressing ethical considerations regarding data use and privacy. The integration of AI tools in clinical settings necessitates a multidisciplinary approach, ensuring that technological advancements align with patient-centered care and ethical standards.

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Section: Future Developmentmentioning

confidence: 99%

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

Biró,

Cuesta-Vargas,

Szilágyi

2023

Acta Universitatis Sapientiae, Informatica

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“…A modified signal or a large number of voice signals will be an ideal voice signal representation. Signals or a collection of parameters with respect to the principal signal with the ultimate objective that terrifically significant information can be found in a more obvious and ordered manner [1]. The idea is to design a language independent script to read so as to extract the spectrum from all the 18 points of articulation.…”

Section: Techniques For Voice Analysismentioning

confidence: 99%

“…The Zerointersection Rate procedure accumulates ghostly information. Short-Time Auto time signal correlation is the energy spectrum for inverse Fourier transform containing the periodicity information, harmonics and amplitude [1].…”

Section: Time-domain Features For Voice Analysismentioning

confidence: 99%

Voice Based Disease Identification System

Bendale¹

2021

TURCOMAT

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Human voice as well as the sound of the body is used as a clinical method to assess the health condition of an individual. The evaluation of the human voice has risen as a critical field of exploration. Speech analysis fundamentally involves the extraction of certain features from voice signals for generation of voice in alluring pertinence by using reasonable techniques. This paper brings up normal ailments that sway understanding voice patterns in proof for driving research that have affirmed voice modifications as demonstrative manifestations in their respective ailments and also the technique by which voice analysis can be done.

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“…For the classification of normal and stressed voice, he used the vocal tract to simulate speech production. Dixit (2014) examined the variations between PD patients v. normal subjects using Praat as the software for extracting features from the voice signal, taking into consideration the voice parameter analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Parkinson Disease Based on Analysis and Synthesis of Voice Signal

Mittal

Sharma

2021

International Journal of Healthcare Information Systems and Informatics

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The most important application of voice profiling is pathological voice detection. Parkinson's disease is a chronic neurological degenerative disease affecting the central nervous system responsible for essentially progressive evolution movement disorders. 70% to 90% of Parkinson’s disease (PD) patients show an affected voice. This paper proposes a methodology for PD based on acoustic, glottal, physical, and electrical parameters. The results show that the acoustic parameter is more important in the case of Parkinson’s disease as compared to glottal and physical parameters. The authors achieved 97.2% accuracy to differentiate Parkinson and healthy voice using jitter to pitch ratio proposed algorithm. The Authors also proposed an algorithm of poles calculation of the vocal tract to find formants of the vocal tract. Further, formants are used for finding the transfer function of vocal tract filter. In the end, the authors suggested parameters of the electrical vocal tract model are also changed in the case of PD voices.

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Voice Parameter Analysis for the disease detection

Cited by 13 publications

References 43 publications

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

Voice Based Disease Identification System

Classification of Parkinson Disease Based on Analysis and Synthesis of Voice Signal

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