8/2/2023 0 Comments Using praat for voice analysis![]() ![]() This implied possible different thresholds of detecting dysphonic severity across different acoustic analysis programs. While CPPS and CPP values from these programs were correlated, they did not show absolute agreement. Bland–Altman plots showed that there were differences between programs in CPPS and CPP values. CPP values obtained from VoiceSauce were highly correlated with those from SpeechTool in both tasks. ![]() Connected speech CPPS from these two programs showed moderate correlation in cohort 1 and good correlation in cohort 2. There was good correlation between ADSV and SpeechTool with respect to vowel CPPS in both cohorts. Intraclass correlation coefficients, linear regression, and Bland–Altman plots were used for testing the correlation and agreement between these programs. Voice data recorded from sustained /a/ vowel and connected speech of two cohorts of vocally healthy female participants were analysed using program default settings to measure smoothed CPP (CPPS) in ADSV, CPPS and CPP in SpeechTool, and CPP in VoiceSauce. In doing so, the current findings indicate that the present smartphone application could be used reliably by patients, healthcare providers, and researchers for acoustic voice analysis.This study examined the correlation of, and agreement between, cepstral peak prominence (CPP) measures obtained from three acoustic analysis programs: Analysis of Dysphonia in Speech and Voice (ADSV), SpeechTool, and VoiceSauce. The results of this study provide data, based on a systematic acquisition methods, which supports future use of the current proprietary algorithm. In phase two, a proprietary application, developed for the purposes of this study, was compared against the current standard used for acoustic voice analysis (Praat). In conclusion, the results of this phase indicate that robust acoustic voice analysis is feasible on current day smartphones with samples collected under non-soundproof conditions. These results indicate that current barriers to voice signal acquisition can be relaxed upon, without degrading the ability to measure microacoustic changes. This showed small, clinically unimportant differences in most acoustic measures for both the microphone and the recording environment. In phase one, microphones for voice sample collection and the influence of recording environment were evaluated. In the current work, we sought to empirically examine a multi-phase study evaluating smartphones as an avenue to perform voice sample acquisition and on-device acoustic voice analysis. The existing methods of voice sample collection and analysis are often resource intensive, creating barriers to access. It relies on physical properties of voice and the resultant vocal signal. It is a method of objectively quantifying normal and pathologic voices. ![]() Some improvements are necessary before continuous speech analysis can be considered valid.Īcoustic voice analysis is a part of a thorough voice examination, which can be used in conjunction with aerodynamic measurements, auditory-perceptual analysis, and patient reported outcomes. The proprietary algorithm represents a reliable method to analyze sustained vowel samples. Quiet, non-soundproof settings can be used for voice collection. Smartphone microphones are adequate for voice sample collection. Continuous speech analysis was less reliable. The proprietary algorithm reliably analyzed sustained vowels, with strong correlation to the Praat results. Microphone and recording environment had small, clinically unimportant impacts on most measurements. Prospective samples were used to test the proprietary algorithm, whereby samples were analyzed using this and Praat. Microphone and recording environment were evaluated using previously collected voice samples presented in four conditions to test two microphones and two recording environments. A proprietary analysis algorithm is presented for validation. This project evaluates smartphone microphone and recording environment impacts on voice sample collection for acoustic voice analysis. Acoustic voice analysis requires a resource intensive setup, including a soundproof booth. ![]()
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