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Loss of the larynx significantly alters natural voice production, requiring alternative communication modalities and rehabilitation methods to restore speech intelligibility and improve the quality of life of affected individuals. This paper explores advances in alaryngeal speech enhancement to improve signal quality and reduce background noise, focusing on individuals who have undergone laryngectomy. In this study, speech samples were obtained from 23 Lithuanian males who had undergone laryngectomy with secondary implantation of the tracheoesophageal prosthesis (TEP). Pareto-optimized gated long short-term memory was trained on tracheoesophageal speech data to recognize complex temporal connections and contextual information in speech signals. The system was able to distinguish between actual speech and various forms of noise and artifacts, resulting in a 25% drop in the mean signal-to-noise ratio compared to other approaches. According to acoustic analysis, the system significantly decreased the number of unvoiced frames (proportion of voiced frames) from 40% to 10% while maintaining stable proportions of voiced frames (proportion of voiced speech frames) and average voicing evidence (average voice evidence in voiced frames), indicating the accuracy of the approach in selectively attenuating noise and undesired speech artifacts while preserving important speech information., Competing Interests: Declaration of Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

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.

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., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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.

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.

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%.

Purpose: The study aimed to evaluate the impact of different variables on the longevity of Voice Prosthesis (VP) in patients after total laryngectomy., Patients and Methods: This retrospective cohort study is based on data about a continuous series of 328 third-generation VP, which were implanted between 2016 and 2020. Data about the VP users' age, sex, place of residence, laryngeal tumor stage, neck irradiation, VP size, and the use of Heat and Moisture Exchanger (HME) were obtained and analyzed. The effect of these variables on VP lifetime was determined., Results: The median lifetime of VPs in patients 65 years old and above was 182 days (95% CI 168-196), versus 146 days (95% CI 130-162) (P = 0.033) in patients younger than 65. Neck irradiation was associated with a longer VP median lifetime of 161 days (95% CI 142-180) compared to 126 days (95% CI 100-152) with no prior neck irradiation (P = 0.046). HME usage was associated with significantly increased longevity of VPs: 182 days (95% CI 156-208) with HME and 149 days (95% CI 132-166) without HME usage (P = 0.039)., Conclusion: The results of the present study suggest that neck irradiation, and routine use of use of HME are positively associated with the longevity of VPs., Competing Interests: Declarations of Interest The authors declare no conflict of interest., (Copyright © 2022 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Background and Objective: The literature lacks data about the evaluation of throat-related symptoms proving chronic tonsillitis as the most common indication for adult tonsillectomy. Therefore, the aim of this study was to assess the most important throat-related symptoms suggestive of chronic tonsillitis in adults., Material and Methods: A prospective cohort study was carried out. The analysis of throat-related symptoms (complaints, tonsillitis rate, pharyngeal signs, and antistreptolysin-O titer) in 81 adults with histologically confirmed chronic tonsillitis was conducted., Results: Recurrent tonsillitis was the most common complaint (74.1%). The mean number of tonsillitis episodes was 3.6 (SD, 1.9) times per year. There were no significant differences comparing the frequencies of all the analyzed pharyngeal signs (P>0.05). The antistreptolysin-O titer (mean, 279.8; SD, 211.6 UL) was pathological in 33.3% of patients. The study identified the most important throat-related symptoms revealing chronic tonsillitis: tonsillar cryptic debris (OR, 8.84; 95% CI, 1.93-40.53; P=0.005) and enlarged anterior cervical lymph nodes along with the frequency of tonsillitis episodes exceeding 3 times per year (OR, 8.27; 95% CI, 1.33-51.57; P=0.024). The classification accuracy of 85.2% was obtained., Conclusions: Tonsillar cryptic debris and enlarged regional lymph nodes along with recurrent tonsillitis could support the diagnosis of chronic tonsillitis in adults when considering tonsillectomy.