An iOS-based VoiceScreen application: feasibility for use in clinical settings—a pilot study

Abstract

Objectives To elaborate the application suitable for smartphones for estimation of Acoustic Voice Quality Index (AVQI) and evaluate its usability in the clinical setting. Methods An elaborated AVQI automatization and background noise monitoring functions were implemented into a mobile “ VoiceScreen” application running the iOS operating system. A study group consisted of 103 adult individuals with normal voices ( n = 30) and 73 patients with pathological voices. Voice recordings were performed in the clinical setting with “ VoiceScreen” app using iPhone 8 microphones. Voices of 30 patients were recorded before and 1 month after phonosurgical intervention. To evaluate the diagnostic accuracy differentiating normal and pathological voice, the receiver-operating characteristic statistics, i.e., area under the curve (AUC), sensitivity and specificity, and correct classification rate (CCR) were used. Results A high level of precision of AVQI in discriminating between normal and dysphonic voices was yielded with corresponding AUC = 0.937. The AVQI cutoff score of 3.4 demonstrated a sensitivity of 86.3% and specificity of 95.6% with a CCR of 89.2%. The preoperative mean value of the AVQI [6.01(SD 2.39)] in the post-phonosurgical follow-up group decreased to 2.00 (SD 1.08). No statistically significant differences ( p = 0.216) between AVQI measurements in a normal voice and 1-month follow-up after phonosurgery groups were revealed. Conclusions The “ VoiceScreen ” app represents an accurate and robust tool for voice quality measurement and demonstrates the potential to be used in clinical settings as a sensitive measure of voice changes across phonosurgical treatment outcomes. Supplementary Information The online version contains supplementary material available at 10.1007/s00405-022-07546-w.

Accuracy Analysis of the Multiparametric Acoustic Voice Indices, the VWI, AVQI, ABI, and DSI Measures, in Differentiating between Normal and Dysphonic Voices

“…The multivariate structures of the VWI, AVQI, and ABI, on the other hand, depend on a linear regression model which incorporates pertinent acoustic parameters; they consist of both continuous speech and sustained vowel sounds in the acoustic evaluation, and the processing of signals employs freeware Praat algorithms, and can thus be standardized and made automated. This has already been realized in several applications available for AVQI estimation: VoiceEvalU8 [ 48 ], A Comprehensive Application for Grading Severity of Voice [ 29 ], VoiceScreen, version 4.4.22 [ 49 ], and ABI assessment: VOXplot, version 2.0 [ 50 ]. As a result, the registration of the AVQI, ABI, and VWI as an “ecologically valid” MAVI may be readily accomplished using specific programs, even without the presence of trained staff, allowing individuals to self-assess their voice quality.…”

Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson’s Disease: A Study on Speaker Diarization and Classification Techniques

“…Particularly, the suitability of smartphone microphones for capturing voice recordings, even in the presence of ambient noise, and their capability to successfully record lung sounds, making auscultation feasible, are widely demonstrated in the literature [ 4 , 5 ]. Moreover, they have been found to be adequate for the digitization of pathologic voices, making them valuable for clinical voice research [ 6 ]. The high performance of smartphone microphones has been further proven in studies showing how they outperform standard external microphones in recording and analyzing normal and dysphonic voices [ 7 ].…”

Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones

“…Pitch perturbation (local jitter and periodic standard deviation) and the harmonics-to-noise ratio from Dejonckere and Lebacq appear to be the most reliable acoustic metrics. The study on the end-user mobile app "VoiceScreen" also demonstrated an accurate and robust method for measuring voice quality and the potential to be used in clinical settings as a sensitive assessment of voice alterations throughout the results of phonosurgical treatment [15].…”