Speech Parameters Extraction for Text-to-Speech Synthesis for Punjabi

Kaur, Navdeep; Singh, Parminder

doi:10.1007/978-981-15-9554-7_66

Cited by 1 publication

(3 citation statements)

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“…There are two different mechanisms that contribute to the link between the speech signal and the heartbeat. The first mechanism, described in several studies, 18–22 is based on the periodic influence of the blood pulse on the vocal cords and the larynx (“voice box”), two vital organs for voice production. Each heartbeat initiates a blood pulse at the larynx and the vocal cord muscles, changing their mass periodically.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, adoption of smartwatch‐driven AF monitors by the over 65‐year‐old population is rather limited 32 . Voice analysis has been used as a monitoring tool for various medical conditions 33 and has been reported to enable estimation of heart rate 18‐22 …”

Section: Discussionmentioning

confidence: 99%

“…Analysis of voice signals from natural speech has been reported to enable estimation of heart rate, 18–22 and specific frequency‐related characteristics of the voice signal have been associated with coronary artery disease 23 …”

Section: Introductionmentioning

confidence: 99%

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Automated detection of atrial fibrillation based on vocal features analysis

Golovchiner

Glikson

Swissa

et al. 2022

Cardiovasc electrophysiol

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Introduction: Early detection of atrial fibrillation (AF) is desirable but challenging due to the often-asymptomatic nature of AF. Known screening methods are limited and most of them depend of electrocardiography or other techniques with direct contact with the skin. Analysis of voice signals from natural speech has been reported for several applications in medicine.The study goal was to evaluate the usefulness of vocal features analysis for the detection of AF.Methods: This prospective study was performed in two medical centers. Patients with persistent AF admitted for cardioversion were enrolled. The patients pronounced the vowels "Ahh" and "Ohh" were recorded synchronously with an ECG tracing. An algorithm was developed to provide an "AF indicator" for detection of AF from the speech signal.Results: A total of 158 patients were recruited. The final analysis of "Ahh" and "Ohh" syllables was performed on 143 and 142 patients, respectively. The mean age was 71.4 ± 9.3 and 43% of patients were females. The developed AF indicator was reliable. Its numerical value decreased significantly in sinus rhythm (SR) after the cardioversion ("Ahh": from 13.98 ± 3.10 to 7.49 ± 1.58; "Ohh": from 11.39 ± 2.99 to 2.99 ± 1.61). The values at SR were significantly more homogenous compared to AF as indicated by a lower standard deviation. The area under the receiver operating characteristic curve was >0.98 and >0.89 ("Ahh" and "Ohh," respectively, p < .001).The AF indicator sensitivity is 95% with 82% specificity. Conclusion:This study is the first report to demonstrate feasibility and reliability of the identification of AF episodes using voice analysis with acceptable accuracy, within the identified limitations of our study methods. The developed AF indicator has higher accuracy using the "Ahh" syllable versus "Ohh."

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%