Smartphone-Based Voice Wellness Index Application for Dysphonia Screening and Assessment: Development and Reliability.

OBJECTIVE: This study aimed to develop a Voice Wellness Index (VWI) application combining the acoustic voice quality index (AVQI) and glottal function index (GFI) data and to evaluate its reliability in quantitative voice assessment and normal versus pathological voice differentiation. STUDY DESIGN: Cross-sectional study. METHODS: A total of 135 adult participants (86 patients with voice disorders and 49 patients with normal voices) were included in this study. Five iOS and Android smartphones with the "Voice Wellness Index" app installed were used to estimate VWI. The VWI data obtained using smartphones were compared with VWI measurements computed from voice recordings collected from a reference studio microphone. The diagnostic efficacy of VWI in differentiating between normal and disordered voices was assessed using receiver operating characteristics (ROC). RESULTS: With a Cronbach's alpha of 0.972 and an ICC of 0.972 (0.964-0.979), the VWI scores of the individual smartphones demonstrated remarkable inter-smartphone agreement and reliability. The VWI data obtained from different smartphones and a studio microphone showed nearly perfect direct linear correlations (r = 0.993-0.998). Depending on the individual smartphone device used, the cutoff scores of VWI related to differentiating between normal and pathological voice groups were calculated as 5.6-6.0 with the best balance between sensitivity (94.10-95.15%) and specificity (93.68-95.72%), The diagnostic accuracy was excellent in all cases, with an area under the curve (AUC) of 0.970-0.974. CONCLUSION: The "Voice Wellness Index" application is an accurate and reliable tool for voice quality measurement and normal versus pathological voice screening and has considerable potential to be used by healthcare professionals and patients for voice assessment.

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals.

The problem of cleaning impaired speech is crucial for various applications such as speech recognition, telecommunication, and assistive technologies. In this paper, we propose a novel approach that combines Pareto-optimized deep learning with non-negative matrix factorization (NMF) to effectively reduce noise in impaired speech signals while preserving the quality of the desired speech. Our method begins by calculating the spectrogram of a noisy voice clip and extracting frequency statistics. A threshold is then determined based on the desired noise sensitivity, and a noise-to-signal mask is computed. This mask is smoothed to avoid abrupt transitions in noise levels, and the modified spectrogram is obtained by applying the smoothed mask to the signal spectrogram. We then employ a Pareto-optimized NMF to decompose the modified spectrogram into basis functions and corresponding weights, which are used to reconstruct the clean speech spectrogram. The final noise-reduced waveform is obtained by inverting the clean speech spectrogram. Our proposed method achieves a balance between various objectives, such as noise suppression, speech quality preservation, and computational efficiency, by leveraging Pareto optimization in the deep learning model. The experimental results demonstrate the effectiveness of our approach in cleaning alaryngeal speech signals, making it a promising solution for various real-world applications.

Reliability of Universal-Platform-Based Voice Screen Application in AVQI Measurements Captured with Different Smartphones.

The aim of the study was to develop a universal-platform-based (UPB) application suitable for different smartphones for estimation of the Acoustic Voice Quality Index (AVQI) and evaluate its reliability in AVQI measurements and normal and pathological voice differentiation. Our study group consisted of 135 adult individuals, including 49 with normal voices and 86 patients with pathological voices. The developed UPB "Voice Screen" application installed on five iOS and Android smartphones was used for AVQI estimation. The AVQI measures calculated from voice recordings obtained from a reference studio microphone were compared with AVQI results obtained using smartphones. The diagnostic accuracy of differentiating normal and pathological voices was evaluated by applying receiver-operating characteristics. One-way ANOVA analysis did not detect statistically significant differences between mean AVQI scores revealed using a studio microphone and different smartphones (F = 0.759; p = 0.58). Almost perfect direct linear correlations (r = 0.991-0.987) were observed between the AVQI results obtained with a studio microphone and different smartphones. An acceptable level of precision of the AVQI in discriminating between normal and pathological voices was yielded, with areas under the curve (AUC) displaying 0.834-0.862. There were no statistically significant differences between the AUCs (p > 0.05) obtained from studio and smartphones' microphones. The significant difference revealed between the AUCs was only 0.028. The UPB "Voice Screen" application represented an accurate and robust tool for voice quality measurements and normal vs. pathological voice screening purposes, demonstrating the potential to be used by patients and clinicians for voice assessment, employing both iOS and Android smartphones.

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

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.

Accuracy of Acoustic Voice Quality Index Captured With a Smartphone - Measurements With Added Ambient Noise.

OBJECTIVE: To evaluate the accuracy of Acoustic Voice Quality Index (AVQI) measures obtained from voice recordings simultaneously using oral and smartphone microphones in a sound-proof room, and to compare them with AVQIs obtained from the same smartphone voice recordings with added ambient noise. METHODS: A study group of 183 subjects with normal voices (n = 86) and various voice disorders (n = 97) was asked to read aloud a standard text and sustain the vowel /a/. The controlled ambient noise averaged at 29.61 dB SPL was added digitally to the smartphone voice recordings. Repeated measures analysis of variances (ANOVA) with Greenhouse-Geiser correction was used to evaluate AVQI changes within subjects. To evaluate the level of agreement between AVQI measurements obtained from different voice recordings Bland-Altman plots were used. RESULTS: Repeated measures ANOVA showed that differences among AVQI results obtained from voice recordings done with oral studio microphone, recordings done with a smartphone microphone, and recordings done with a smartphone microphone with added ambient noise were not statistically significant (P = 0.07). No significant systemic differences and acceptable level of random errors in AVQI measurements of voice recordings made with oral and smartphone microphones (including added noise) were revealed. CONCLUSION: The AVQI measures obtained from smartphone microphones voice recordings with experimentally added ambient noise revealed an acceptable agreement with results of oral microphone recordings, thus suggesting the suitability of smartphone microphone recordings performed even in the presence of acceptable ambient noise for estimation of AVQI.

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

The study aimed to investigate and compare the accuracy and robustness of the multiparametric acoustic voice indices (MAVIs), namely the Dysphonia Severity Index (DSI), Acoustic Voice Quality Index (AVQI), Acoustic Breathiness Index (ABI), and Voice Wellness Index (VWI) measures in differentiating normal and dysphonic voices. The study group consisted of 129 adult individuals including 49 with normal voices and 80 patients with pathological voices. The diagnostic accuracy of the investigated MAVI in differentiating between normal and pathological voices was assessed using receiver operating characteristics (ROC). Moderate to strong positive linear correlations were observed between different MAVIs. The ROC statistical analysis revealed that all used measurements manifested in a high level of accuracy (area under the curve (AUC) of 0.80 and greater) and an acceptable level of sensitivity and specificity in discriminating between normal and pathological voices. However, with AUC 0.99, the VWI demonstrated the highest diagnostic accuracy. The highest Youden index equaled 0.93, revealing that a VWI cut-off of 4.45 corresponds with highly acceptable sensitivity (97.50%) and specificity (95.92%). In conclusion, the VWI was found to be beneficial in describing differences in voice quality status and discriminating between normal and dysphonic voices based on clinical diagnosis, i.e., dysphonia type, implying the VWI's reliable voice screening potential.

Lightweight Deep Learning Model for Assessment of Substitution Voicing and Speech after Laryngeal Carcinoma Surgery.

Laryngeal carcinoma is the most common malignant tumor of the upper respiratory tract. Total laryngectomy provides complete and permanent detachment of the upper and lower airways that causes the loss of voice, leading to a patient's inability to verbally communicate in the postoperative period. This paper aims to exploit modern areas of deep learning research to objectively classify, extract and measure the substitution voicing after laryngeal oncosurgery from the audio signal. We propose using well-known convolutional neural networks (CNNs) applied for image classification for the analysis of voice audio signal. Our approach takes an input of Mel-frequency spectrogram (MFCC) as an input of deep neural network architecture. A database of digital speech recordings of 367 male subjects (279 normal speech samples and 88 pathological speech samples) was used. Our approach has shown the best true-positive rate of any of the compared state-of-the-art approaches, achieving an overall accuracy of 89.47%.

Exploring the feasibility of the combination of acoustic voice quality index and glottal function index for voice pathology screening.

PURPOSE: The aim of this study was to explore the diagnostic value of the combination of Acoustic Voice Quality Index (AVQI) and Glottal Function Index (GFI) as a screening tool for voice disorders, and to compare the AVQI measurements obtained using oral and smartphone (SP) microphones. METHODS: A study group consisted of 183 adult individuals including 86 subjects with normal voice and 97 patients with pathological voice. Voice recordings were performed simultaneously with an oral AKG Perception 220 and SP iPhone 6s microphones. To evaluate the diagnostic accuracy differentiating normal and pathological voice, the receiver-operating characteristic statistics [area under curve (AUC), positive and negative likelihood ratios (LR+ and LR-)], and correct classification rate (CCR) were used. RESULTS: The AVQI cut-off scores of 3.31 for oral and 3.32 for SP microphones were associated with very good test accuracy (AUC = 0.857 and AUC = 0.818), resulting in balance between sensitivity and specificity (70.0% vs 86.0% and 70% vs 87.0%). The CCR reached 78%. The combined AVQI and GFI cut-off scores of 6.65 for oral and 7.1 for SP microphones corresponded with excellent test accuracy (AUC = 0.976 and AUC = 0.965) and sensitivity and specificity (93.0% vs 93.0% and 91.0% vs 94%). Very respectable levels of LR+ and LR- both for oral microphone (13.3 and 0.08) and for SP microphone (15.6 and 0.10) voice recordings were achieved. CCRs of 93% and 92% confirmed the results of ROC statistics. CONCLUSIONS: Combination of AVQI and GFI measurements significantly improved diagnostic accuracy in differentiating normal vs pathological voice.

The Influence of Gender and Age on the Acoustic Voice Quality Index and Dysphonia Severity Index: A Normative Study.

OBJECTIVES: The Acoustic Voice Quality Index (AVQI) and the Dysphonia Severity Index (DSI) are commonly used in research and clinical practice to quantify voice quality. The aim of this study was to investigate the influence of gender and age on AVQI and again on DSI. METHODS: In total, 123 vocally healthy adults (68 females, 55 males, and age ranging between 20 and 79 years) were evaluated. RESULTS: Gender had no effects on AVQI and DSI (both P values > 0.05). Additionally, AVQI showed no significant correlation with age (P > 0.05, r2 = 0.008). However, DSI had a statistically significant correlation with age (P < 0.05), with 5% of the variance in DSI explained by the variance in age. CONCLUSIONS: AVQI values do not depend on gender and age. DSI values do not depend on gender but correlated slightly with age. This finding confirms earlier research.

Diagnostic Accuracy of Dysphonia Classification of DSI and AVQI.

OBJECTIVE: The Dysphonia Severity Index (DSI) and the Acoustic Voice Quality Index (AVQI) have been successfully investigated to quantify voice quality. The aim of the present study was to evaluate the diagnostic accuracy of both measurements in comparison with the dysphonia classification. METHODS: In total, 264 subjects with vocally healthy voices (n = 105) and with various voice disorders (n = 159) were included in the study. To determine the dysphonia classification, all subjects underwent a videolaryngostroboscopy and, if necessary, a direct microlaryngoscopy plus a clinical examination to diagnose a voice disorder. Patients with a vocally healthy voice had no actual voice complaints, no history of chronic laryngeal diseases or voice disorders, no hearing problems and were determined as healthy voices by clinical voice specialists. To evaluate the diagnostic accuracy, receiver operating characteristic statistics and correct classification rate (CCR) were used. RESULTS: The diagnostic accuracy of DSI and AVQI showed strong sensitivity and specificity in the determination of dysphonia classification. A DSI threshold of 3.05 obtained a high sensitivity of 94.3% and specificity of 84.3%. An CCR of 88% was determined for DSI. Also, an AVQI threshold of 3.31 showed reasonable sensitivity of 71.7% and specificity of 88%. The CCR for AVQI was 79%. CONCLUSION: Although DSI and AVQI were developed to quantify voice quality, the present results showed that both measurements can evaluate the dysphonia classification as well. Particularly, the DSI might have higher potential in the evaluation of dysphonia classification. LEVEL OF EVIDENCE: 2C Laryngoscope, 129:692-698, 2019.

A comparison of Dysphonia Severity Index and Acoustic Voice Quality Index measures in differentiating normal and dysphonic voices.

PURPOSE: The aim of the study was to investigate and compare the feasibility and robustness of the Acoustic Voice Quality Index (AVQI) and the Dysphonia Severity Index (DSI) in diagnostic accuracy, differentiating normal and dysphonic voices. METHODS: A group of 264 subjects with normal voices (n = 105) and with various voice disorders (n = 159) were asked to read aloud a text and to sustain the vowel /a/. Both speech tasks were concatenated, and perceptually rated for dysphonia severity by five voice clinicians. They rated the Grade (G) and the overall dysphonia severity with a visual analog scale (VAS). All concatenated voice samples were acoustically analyzed to receive an AVQI score. For DSI analysis, the required voice parameters were obtained from the sustained phonation of the vowel /a/. RESULTS: The results achieved significant and marked concurrent validity between both auditory-perceptual judgment procedures and both acoustic voice measures. The DSI threshold (i.e., DSI = 3.30) pertaining to Gmean obtained reasonable sensitivity of 85.8% and specificity of 83.4%. For VASmean, the DSI threshold of 3.30 was determined also with reasonable sensitivity of 70.3% and excellent specificity of 93.9%. Also, the AVQI threshold (i.e., AVQI = 3.31) pertaining to Gmean demonstrated reasonable sensitivity of 78.1% and excellent specificity of 92.0%. For VASmean, an AVQI threshold of 3.33 was determined with excellent sensitivity of 97.0% and reasonable specificity of 81.8%. CONLUSION: The outcomes of the present study indicate comparable results between DSI and AVQI with a high level of validity to discriminate between normal and dysphonic voices. However, a higher level of accuracy was yielded for AVQI as a correlate of auditory perceptual judgment suggesting a reliable voice screening potential of AVQI.